text:
/* TEMPLATE GENERATED TESTCASE FILE
Filename: CWE134_Uncontrolled_Format_String__char_environment_fprintf_72a.cpp
Label Definition File: CWE134_Uncontrolled_Format_String.label.xml
Template File: sources-sinks-72a.tmpl.cpp
*/
/*
 * @description
 * CWE: 134 Uncontrolled Format String
 * BadSource: environment Read input from an environment variable
 * GoodSource: Copy a fixed string into data
 * Sinks: fprintf
 *    GoodSink: fprintf with "%s" as the second argument and data as the third
 *    BadSink : fprintf with data as the second argument
 * Flow Variant: 72 Data flow: data passed in a vector from one function to another in different source files
 *
 * */

#include "std_testcase.h"
#include <vector>

#ifndef _WIN32
#include <wchar.h>
#endif

#define ENV_VARIABLE "ADD"

#ifdef _WIN32
#define GETENV getenv
#else
#define GETENV getenv
#endif

using namespace std;

namespace CWE134_Uncontrolled_Format_String__char_environment_fprintf_72
{

#ifndef OMITBAD

/* bad function declaration */
void badSink(vector<char *> dataVector);

void bad()
{
    char * data;
    vector<char *> dataVector;
    char dataBuffer[100] = "";
    data = dataBuffer;
    {
        /* Append input from an environment variable to data */
        size_t dataLen = strlen(data);
        char * environment = GETENV(ENV_VARIABLE);
        /* If there is data in the environment variable */
        if (environment != NULL)
        {
            /* POTENTIAL FLAW: Read data from an environment variable */
            strncat(data+dataLen, environment, 100-dataLen-1);
        }
    }
    /* Put data in a vector */
    dataVector.insert(dataVector.end(), 1, data);
    dataVector.insert(dataVector.end(), 1, data);
    dataVector.insert(dataVector.end(), 1, data);
    badSink(dataVector);
}

#endif /* OMITBAD */

#ifndef OMITGOOD

/* goodG2B uses the GoodSource with the BadSink */
void goodG2BSink(vector<char *> dataVector);

static void goodG2B()
{
    char * data;
    vector<char *> dataVector;
    char dataBuffer[100] = "";
    data = dataBuffer;
    /* FIX: Use a fixed string that does not contain a format specifier */
    strcpy(data, "fixedstringtest");
    /* Put data in a vector */
    dataVector.insert(dataVector.end(), 1, data);
    dataVector.insert(dataVector.end(), 1, data);
    dataVector.insert(dataVector.end(), 1, data);
    goodG2BSink(dataVector);
}

/* goodB2G uses the BadSource with the GoodSink */
void goodB2GSink(vector<char *> dataVector);

static void goodB2G()
{
    char * data;
    vector<char *> dataVector;
    char dataBuffer[100] = "";
    data = dataBuffer;
    {
        /* Append input from an environment variable to data */
        size_t dataLen = strlen(data);
        char * environment = GETENV(ENV_VARIABLE);
        /* If there is data in the environment variable */
        if (environment != NULL)
        {
            /* POTENTIAL FLAW: Read data from an environment variable */
            strncat(data+dataLen, environment, 100-dataLen-1);
        }
    }
    dataVector.insert(dataVector.end(), 1, data);
    dataVector.insert(dataVector.end(), 1, data);
    dataVector.insert(dataVector.end(), 1, data);
    goodB2GSink(dataVector);
}

void good()
{
    goodG2B();
    goodB2G();
}

#endif /* OMITGOOD */

} /* close namespace */

/* Below is the main(). It is only used when building this testcase on
   its own for testing or for building a binary to use in testing binary
   analysis tools. It is not used when compiling all the testcases as one
   application, which is how source code analysis tools are tested. */

#ifdef INCLUDEMAIN

using namespace CWE134_Uncontrolled_Format_String__char_environment_fprintf_72; /* so that we can use good and bad easily */

int main(int argc, char * argv[])
{
    /* seed randomness */
    srand( (unsigned)time(NULL) );
#ifndef OMITGOOD
    printLine("Calling good()...");
    good();
    printLine("Finished good()");
#endif /* OMITGOOD */
#ifndef OMITBAD
    printLine("Calling bad()...");
    bad();
    printLine("Finished bad()");
#endif /* OMITBAD */
    return 0;
}

#endif


text:
La Unione Sportiva Dilettantistica Adriese 1906 es un club de fútbol amateur italiano, con sede en la ciudad de Adria (Rovigo), en Véneto. El club fue fundado en 1906 y actualmente juega en la Serie D, la cuarta división de fútbol más importante del país.

Los colores del equipo son granate y azul. Su estadio de local es el Stadio Luigi Bettinazzi en Adria, con una capacidad estimada para 2.200 aficionados.

Historia
En 1906 se fundó la sociedad Águila Adria, primera empresa que llevó el fútbol al centro de la región de Polesine. A principios de esa década, se llevaron a cabo partidos amistosos, pero solo como parte de esa entidad deportiva. En 1915 cambió su nombre por el de S.S. Forti e Liberi Adria, pero la actividad siguió siendo sólo de carácter local. La afiliación a la Federación Italiana de Fútbol. se llevó a cabo en el año 1923, participando sólo en algunos torneos; desde 1925, con el nombre de A.C. Adria y más tarde S.S. Adriese, cuando comenzó a subir en las divisiones menores del Veneto, hasta llegar en 1936 a la Primera División.

Después de permanecer inactivo durante la Segunda Guerra Mundial, el equipo registró en 1945 su nombre definitivo de U.S. Adriese ante la Federación. Durante los años cincuenta participó en numerosos campeonatos interregionales, y luego volver en las ligas menores hasta 1972, cuando conquista la nueva Serie D, para llegar a la Serie C2 en 1978.Permaneció en ligas semi-profesionales durante tres temporadas después de ceder en 1981 y de nuevo en 1982.

La compleja estructura de la Serie D fue reestructurada en 1995, componiéndose de varias ligas regionales relacionadas: Eccellenza, Promozione e Interregionale. Después de participar durante 3 años en Eccellenza, el Adriese ganó el Grupo B en 2009 y estuvo de vuelta en la primera división de aficionados. En la temporada 2009-10 se alejó de nuevo en el Eccellenza, al final de una temporada decepcionante de terminar tercero por la cola.

La temporada 2010/2011 inició el Eccellenza con un grupo joven y con  Olivo Frizzarin como presidente. La temporada resultó desastrosa, con el consiguiente descenso a la Promozione después de terminar en el último lugar de su grupo: como resultado, Olivo Frizzarin dejó el Adriese después de ocho años como presidente, renunciando junto con el Consejo de Administración de la Sociedad en julio de 2011. Posteriormente, se convirtió en un socio la asociación Luciano Scantamburlo, elegido por unanimidad como nuevo presidente de la USD Adriese 1906. Scantamburlo reconfirmó la mayoría de los empleados de la temporada anterior, y logró traer de vuelta con el cargo de director deportivo a Luciano Vianello mientras que en la conducción técnica fue nombrado al entrenador Giuseppe Pregnolato.

Jugadores

Plantilla 2019-20

|-
| colspan=8 bgcolor=#960018 style="color:white;" align=center|  Porteros

|-
| colspan=8 bgcolor=#960018 style="color:white;" align=center|  Defensores

|-
| colspan=8 bgcolor=#960018 style="color:white;" align=center|  Mediocampistas

|-
| colspan=8 bgcolor=#960018 style="color:white;" align=center|  Delanteros

|-
| colspan=8 bgcolor=#003153 style="color:white;" align=center| Unione Sportiva Dilettantistica Adriese 1906

Jugadores famosos
 Mario Ardizzon
 Giacomo Blason
  Giovanni Galeone
  Luigi Pasetti
 Giuseppe Pregnolato
 Gastone Prendato
  Renato Sanero
  Nevio Scala
Mario Scarpa
János Vanicsek

Enlaces externos
Página oficial
Historia del Adriese
Perfil en Ceroacero.es

Equipos de fútbol de Italia
Equipos de fútbol fundados en 1906
Deporte en Véneto
Italia en 1906

redpajama_set_name: RedPajamaWikipedia


text:
El Mas de Sedó és un mas del municipi de Reus (Baix Camp) protegit com a bé cultural d'interès local, situat a la partida del Roquís, a la sortida de Reus, a la carretera de Riudoms km 2,2.

Descripció
Edifici d'enormes proporcions d'estil clàssic amb entrades dissenyades amb columnes d'ordre jònic i una escalinata per a accedir-hi. El mas es va construir a finals del  com a casa residencial de gran magnificència, amb una capella on hi anaven a missa els habitants dels masos del costat. En els seus inicis, era un conjunt de masies que van convertir-se en una sola propietat en afegir-hi el Mas de Rincón, el Mas de Muntaner i altres bocins de terres. En total, el mas i els terrenys ocupaven cent jornals, a la dreta del barranc de Pedret i fins al límit del terme de Riudoms. Tenia un llac i jardins amplíssims i una portalada principal situada a la carretera a Riudoms feta de ferro forjat, amb reixes molt treballades i que tenien a la part central les lletres "J.H.S" que mostraven un origen religiós i datada el 1870. Estava formada per dues portes centrals i dues laterals que s'articulaven amb quatre columnes rodones de ferro colat. Tenia sis metres d'ampla i tres d'alçada. La porta, molt emblemàtica, va desaparèixer, quan el mas va ser propietat de "Nueva Rumasa". El Mas de Sedó Vell és un mas de planta molt irregular, degut a les diverses ampliacions que s'hi han fet al llarg del temps, que havien habitat els masovers del Mas de Sedó.

Història

El mas i la finca van ser formades per Antoni Sedó Pàmies (Reus 1842 - Barcelona 1902) que va promoure la creació de la colònia tèxtil Sedó a Esparreguera. A la mort del propietari va passar a mans de Restituto de Amézaga, un "indiano" que havia fet molts diners i era director d'un banc. Pel novembre de 1936, durant la Guerra civil i com a conseqüència d'una explosió, l'edifici principal va ser destruït. Van morir cinc persones que van ser enterrades al cementiri municipal. El propietari senyor Amézaga va morir uns dies després a conseqüència de l'accident. Al Mas s'hi havia instal·lat una fàbrica d'espoletes per a les bombes d'aviació, i tot indica que l'explosió va ser deguda a una mala manipulació per part d'un operari. Les filles van intentar reconstruir el mas, juntament amb el seu germà José Ramón de Amézaga que va ser el 1939 alcalde de Reus, nomenat directament des de Burgos pel dictador Franco. Al Mas, situat a l'encreuament de les carreteres de Riudoms i Vinyols, s'hi va instal·lar durant la guerra una bateria de potents reflectors, per localitzar els avions enemics que atacaven de nit, com el cèlebre "Isidru", que era el nom popular dels hidroavions alemanys Heinkel He 59 (s'afirmava que sempre era el mateix aparell) i que van devastar el litoral mediterrani per entorpir la rereguarda republicana. Cap a la dècada dels cinquanta, el mas era propietat de la Cooperativa Comarcal d'Avicultors de Reus, que hi va construir instal·lacions per a produir pinsos. Durant molt de temps va ser la seu de l'Escola d'Horticultura, que aprofitava els jardins ornamentals. Després va ser un restaurant. Actualment hi ha també instal·lat el Centre de Formació i Estudis Agrorurals de la Generalitat

Referències

Enllaços externs

 Mas de Sedó, part de la història local

Patrimoni monumental de Reus


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text:
\section{Introduction}
Data-to-text generation broadly refers to the task of automatically
producing text from non-linguistic input
\cite{reiter-dale:00,gatt2018survey}. The input may be in various forms
including databases of records, spreadsheets, expert system knowledge bases,
simulations of physical systems, and so on. Table~\ref{fig:example}
shows an example in the form of a database containing statistics on
NBA basketball games, and a corresponding game summary.

\begin{table*}
\begin{small}
\begin{minipage}[c]{0.37\linewidth}

\begin{tabular}{@{}l@{~~}r@{~~}c@{~~}r@{~~}c@{~~}c@{~~}c@{~~}l@{}} \hline
\lform{TEAM}      & \lform{WIN} &\lform{LOSS} &\lform{PTS} & \lform{FG\_PCT} & \lform{RB} & \lform{AST} & \lform{$\dots$} \\ \hline
\lform{Pacers} &\lform{4} &\lform{6} &\lform{99} & \lform{42} & \lform{40} & \lform{17}& \lform{$\dots$} \\
\lform{Celtics} &\lform{5} &\lform{4} &\lform{105} & \lform{44} &\lform{47} &\lform{22} &\lform{$\dots$} \\ \hline
\end{tabular}

\begin{tabular}{@{}l@{~~}c@{~~}c@{~}r@{~~}c@{~~}c@{~~}l@{~~}l@{}} \hline
\lform{PLAYER} & \lform{H/V} & \lform{AST} & \lform{RB} & \lform{PTS} & \lform{FG} & \lform{CITY} & \lform{$\dots$}\\ \hline
\lform{Jeff Teague} & \lform{H} & \lform{4} & \lform{3} & \lform{20} & \lform{4} & \lform{Indiana} & \lform{$\dots$}\\
\lform{Miles Turner} & \lform{H} & \lform{1} & \lform{8} & \lform{17} & \lform{6} & \lform{Indiana} & \lform{$\dots$}\\
\lform{Isaiah Thomas} & \lform{V}& \lform{5} & \lform{0} & \lform{23} & \lform{4} & \lform{Boston} & \lform{$\dots$}\\
\lform{Kelly Olynyk} & \lform{V}& \lform{4} & \lform{6} & \lform{16} & \lform{6} & \lform{Boston} & \lform{$\dots$}\\
\lform{Amir Johnson} & \lform{V}& \lform{3} & \lform{9} & \lform{14} & \lform{4} & \lform{Boston} & \lform{$\dots$}\\
\lform{$\dots$} & \lform{$\dots$} & \lform{$\dots$} & \lform{$\dots$} & \lform{$\dots$}& \lform{$\dots$} & \lform{$\dots$}\\\hline

\end{tabular}\\
\lform{PTS:} points, \lform{FT\_PCT:} free throw percentage,
\lform{RB:}  rebounds, \lform{AST:}
assists, \lform{H/V:} home or visiting, \lform{FG:} field goals,
\lform{CITY:} player team city.
\end{minipage}
\begin{minipage}[c]{0.1\linewidth}
  \begin{tabular}{p{10.7cm}} \hline The \textbf{Boston Celtics}
    defeated the host \textbf{Indiana Pacers} \textbf{105-99} at
    Bankers Life Fieldhouse on Saturday. In a battle between two
    injury-riddled teams, the Celtics were able to prevail with a much
    needed road victory. The key was shooting and defense, as the
    \textbf{Celtics} outshot the \textbf{Pacers} from the field, from
    three-point range and from the free-throw line. Boston also held
    Indiana to \textbf{42 percent} from the field and \textbf{22
      percent} from long distance. The Celtics also won the rebounding
    and assisting differentials, while tying the Pacers in
    turnovers. There were 10 ties and 10 lead changes, as this game
    went down to the final seconds. Boston (\textbf{5--4}) has had to deal with
    a gluttony of injuries, but they had the fortunate task of playing
    a team just as injured here. \textbf{Isaiah} Thomas led the team in
    scoring, totaling \textbf{23 points and five assists on 4--of--13}
    shooting. He got most of those points by going 14--of--15 from the
    free-throw line. \textbf{Kelly Olynyk} got a rare start and finished second
    on the team with his \textbf{16 points, six rebounds and four assists}.
 %
\end{tabular}
\end{minipage}
\end{small}
\label{fig:example}
\caption{Example of data-records and document
  summary. Entities and values corresponding to the plan in
  Table~\ref{tbl:example-content-plan} are boldfaced.}
\end{table*}

Traditional methods for data-to-text generation \cite{P83-1022,mckeown1992text} implement a pipeline
of modules including content planning (selecting specific content from some input and determining the
structure of the output text), sentence planning (determining the structure and lexical content of each sentence)
 and surface realization (converting the sentence plan to a surface string).
Recent neural generation systems (Lebret et
al.~\citeyear{D16-1128}; Mei et al.~\citeyear{N16-1086}; Wiseman et
al.~\citeyear{wiseman2017challenges}) do not explicitly model any of
these stages, rather they are trained in an end-to-end fashion using
the very successful encoder-decoder architecture (Bahdanau et
al.~\citeyear{bahdanau2015neural}) as their backbone.

\begin{figure}[t]
\includegraphics[scale=0.7]{intro-cropped}
\caption{Block diagram of our approach.}
\label{fig:rotowire-example}
\end{figure}

Despite producing overall fluent text, neural systems have difficulty
capturing long-term structure and generating documents more than a few
sentences long. Wiseman et al.~\shortcite{wiseman2017challenges} show
that neural text generation techniques perform poorly at content
selection, they struggle to maintain inter-sentential coherence, and
more generally a reasonable ordering of the selected facts in the
output text. Additional challenges include avoiding redundancy and
being faithful to the input. Interestingly, comparisons against
template-based methods show that neural techniques do not fare well on
metrics of content selection recall and factual output generation
(i.e.,~they often hallucinate statements which are not supported by
the facts in the database).

In this paper, we address these shortcomings by \emph{explicitly}
modeling content selection and planning within a neural data-to-text
architecture.  Our model learns a content plan from the input and
conditions on the content plan in order to generate the output
document (see Figure~\ref{fig:example} for an illustration). An
explicit content planning mechanism has at least three advantages for
multi-sentence document generation: it represents a high-level
organization of the document structure allowing the decoder to
concentrate on the easier tasks of sentence planning and surface
realization; it makes the process of data-to-document generation more
interpretable by generating an intermediate representation; and
reduces redundancy in the output, since it is less likely for the
content plan to contain the same information in multiple places.

We train our model end-to-end using neural networks and evaluate
its performance on \textsc{RotoWire} (Wiseman et
al.~\citeyear{wiseman2017challenges}), a recently released dataset
which contains statistics of NBA basketball games paired with
human-written summaries (see Table~\ref{fig:example}). Automatic and
human evaluation shows that modeling content selection and
planning improves generation considerably over competitive baselines.

\section{Related Work}

The generation literature provides multiple examples of content
selection components developed for various domains which are either
hand-built
\cite{P83-1022,mckeown1992text,Reiter:1997:BAN:974487.974490,W03-1016}
or learned from data (Barzilay and Lapata~\citeyear{H05-1042}; Duboue
and McKeown~\citeyear{duboue2001empirically,W03-1016}; Liang et
al.~\citeyear{P09-1011}; Angeli et al.~\citeyear{D10-1049}; Kim and
Mooney~\citeyear{C10-2062}; Konstas and
Lapata \citeyear{Konstas:2013:GMC:2591248.2591256}). Likewise,
creating summaries of sports games has been a topic of interest since
the early beginnings of generation systems
(Robin~\citeyear{robin1994revision}; Tanaka-Ishii et
al.~\citeyear{P98-2209}).

Earlier work on content planning has relied on generic planners \cite{dale1988generating},
based on Rhetorical Structure Theory \cite{hovy1993automated} and schemas \cite{mckeown1997language}.
They defined content planners by analysing the target texts and devising hand-crafted rules.
Duboue and McKeown \shortcite{duboue2001empirically}
studied ordering constraints for content plans and Duboue and McKeown \shortcite{duboue2002content} learn a content planner
from an aligned corpus of inputs and human outputs.
A few researchers \cite{mellish1998experiments,karamanis2004entity} rank
content plans according to a ranking function.

More recent work focuses on end-to-end systems instead of individual components.
However, most models make simplistic assumptions such as generation
without any content selection or planning \cite{Belz:2008:AGW:1520025.1520026,N07-1022}
or content selection without planning  (Konstas and
Lapata~\citeyear{N12-1093}; Angeli et al.~\citeyear{D10-1049}; Kim and
Mooney \citeyear{C10-2062}).
An exception are Konstas and Lapata
\shortcite{Konstas:2013:GMC:2591248.2591256} who incorporate content
plans represented as grammar rules operating on the document
level. Their approach works reasonably well with weather forecasts,
but does not scale easily to larger databases, with richer vocabularies, and
longer text descriptions. The model relies on the EM algorithm
\cite{dempster1977maximum} to learn the weights of the grammar rules
which can be very many even when tokens are aligned to database
records as a preprocessing step.

Our work is closest to recent neural network models which learn
generators from data and accompanying text resources. Most previous
approaches generate from Wikipedia infoboxes focusing either on single
sentences (Lebret et al.~\citeyear{D16-1128,E17-1060}; Sha et
al.~\citeyear{sha2017order}; Liu et al.~\citeyear{liu2017table}) or
short texts \cite{perez2018bootstrapping}.  Mei et
al.~\shortcite{N16-1086} use a neural encoder-decoder model to
generate weather forecasts and soccer commentaries, while Wiseman et
al.~\shortcite{wiseman2017challenges} generate NBA game summaries (see
Table~\ref{fig:rotowire-example}). They introduce a new dataset for
data-to-document generation which is sufficiently large for neural
network training and adequately challenging for testing the
capabilities of document-scale text generation (e.g., the average
summary length is~330 words and the average number of input records
is~628). Moreover, they propose various automatic evaluation measures
for assessing the quality of system output. Our model follows on from
Wiseman et al.~\shortcite{wiseman2017challenges} addressing the
challenges for data-to-text generation identified in their work.  We
are not aware of any previous neural network-based approaches which
incorporate content selection and planning mechanisms and generate
multi-sentence documents. \citeauthor{perez2018bootstrapping}
\shortcite{perez2018bootstrapping} introduce a content selection
component (based on multi-instance learning) without content planning,
while \citeauthor{liu2017table} \shortcite{liu2017table} propose a
sentence planning mechanism which orders the contents of a Wikipedia
infobox in order to generate a single sentence.

\section{Problem Formulation}

The input to our model is a table of records (see
Table~\ref{fig:example} left hand-side). Each record~$r_j$ has four
features including its type ($r_{j,1}$; e.g.,~\lform{\small LOSS},
\lform{\small CITY}), entity ($r_{j,2}$; e.g.,~\lform{\small Pacers},
\lform{\small Miles Turner}), value ($r_{j,3}$; e.g.,~\lform{\small
  11}, \lform{\small Indiana}), and whether a player is on the home-
or away-team ($r_{j,4}$; see column~\lform{\small H/V} in
Table~\ref{fig:example}), represented as \{$r_{j,k}\}_{k=1}^{4}$. The
output $y$ is a document containing words $y = y_1 \cdots y_{|y|}$
where $|y|$~is the document length.  Figure~\ref{fig:overall-method}
shows the overall architecture of our model which consists of two
stages: (a) \emph{content selection and planning} operates on the input
records of a database and produces a content plan specifying which
records are to be verbalized in the document and in which order (see
Table~\ref{tbl:example-content-plan}) and (b)~\emph{text generation}
produces the output text given the content plan as input; at each
decoding step, the generation model attends over vector
representations of the records in the content plan.

\begin{figure}[t]
\begin{center}
\includegraphics[scale=.42]{updated_network_cropped}
\end{center}
\caption{Generation model with content selection and planning;
  the content selection gate is illustrated in
  Figure~\ref{fig:content-selection}.}
\label{fig:overall-method}
\end{figure}

Let~$r=\{r_j\}_{j=1}^{|r|}$ denote a table of input records and~$y$
the output text. We model~$p(y|r)$ as the joint probability of
text~$y$ and content plan~$z$, given input~$r$. We further
decompose~$p(y,z|r)$ into~$p(z|r)$, a content selection and planning
phase, and $p(y|r,z)$, a text generation phase:
\begin{equation}
p(y|r) = \sum_z p(y,z|r) = \sum_z p(z|r)p(y|r,z) \nonumber
\end{equation}
In the following we explain how the components~$p(z|r)$ and $p(y|r,z)$
are estimated.

\subsection{Record Encoder}
\label{sec:record-encoder}

The input to our model is a table of unordered records, each
represented as features~\{$r_{j,k}\}_{k=1}^{4}$. Following previous
work (Yang et al.~\citeyear{D17-1197}; Wiseman et
al.~\citeyear{wiseman2017challenges}), we embed features into vectors,
and then use a multilayer perceptron to obtain a vector representation
$\mathbf{r}_j$ for each record:
\begin{equation}
\mathbf{r}_j = \myrelu(\mathbf{W}_r[\mathbf{r}_{j,1};\mathbf{r}_{j,2};\mathbf{r}_{j,3};\mathbf{r}_{j,4}]+\mathbf{b}_r) \nonumber
\end{equation}
where $[;]$ indicates vector concatenation, $\mathbf{W}_r \in \mathbb{R}^{n \times 4n} , \mathbf{b}_r \in \mathbb{R}^{n}$ are parameters, and $\myrelu$ is the rectifier activation function.

\subsection{Content Selection Gate}
\label{content-selection-gate}
The context of a record can be useful in determining its importance
vis-a-vis other records in the table. For example, if a player scores
many points, it is likely that other meaningfully related records such
as field goals, three-pointers, or rebounds will be mentioned in the
output summary. To better capture such dependencies among records, we
make use of the content selection gate mechanism as shown in
Figure~\ref{fig:content-selection}.

We first compute the attention scores~$\alpha_{j,k}$ over the
input table and use them to obtain an attentional vector $\mathbf{r}_j^{att}$~for each record~$r_j$:
\begin{align}
\alpha_{j,k} &\propto \exp (\mathbf{r}_j^\intercal \mathbf{W}_a \mathbf{r}_k) \nonumber \\
\mathbf{c}_j &= \sum_{k \neq j} \alpha_{j,k}\mathbf{r}_{k} \nonumber \\
\mathbf{r}_j^{att} &= \mathbf{W}_g [\mathbf{r}_j; \mathbf{c}_j] \nonumber
\end{align}
where $\mathbf{W}_a \in \mathbb{R}^{n \times n} , \mathbf{W}_g \in \mathbb{R}^{n \times 2n}$ are parameter matrices, and $\sum_{k \neq j} \alpha_{j,k} = 1$.

We next apply the content selection gating mechanism
to~$\mathbf{r}_j$, and obtain the new record
representation~$\mathbf{r}_j^{cs}$ via:
\begin{align}
\mathbf{g}_j &= \sigmoid \left( \mathbf{r}_j^{att} \right) \nonumber \\
\mathbf{r}_j^{cs} &= \mathbf{g}_j \odot \mathbf{r}_j \nonumber
\end{align}
where $\odot$ denotes element-wise multiplication, and gate~$\mathbf{g}_j
\in {[0,1]}^{n}$ controls the amount of information flowing
from~$\mathbf{r}_j$.  In other words, each element in~$\mathbf{r_j}$
is weighed by the corresponding element of the content selection gate
$\mathbf{g}_j$.%

\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{cs_gate-cropped}
\caption{Content selection mechanism.
\label{fig:content-selection}}
\end{figure}

\subsection{Content Planning}
In our generation task, the output text is long but follows a
canonical structure.  Game summaries typically begin by discussing
which team won/lost, following with various statistics involving
individual players and their teams (e.g., who performed exceptionally
well or under-performed), and finishing with any upcoming games.  We
hypothesize that generation would benefit from an explicit plan
specifying both {\it what to say} and {\it in which order}. Our model
learns such content plans from training data.  However, notice that
\textsc{RotoWire} (see Table~\ref{fig:example}) and most similar
data-to-text datasets do not naturally contain content plans.
Fortunately, we can obtain these relatively straightforwardly
following an information extraction approach (which we explain in
Section~\ref{sec:experimental-setup}).

Suffice it to say that plans are extracted by mapping the text in the
summaries onto entities in the input table, their values, and types
(i.e.,~relations). A plan is a sequence of pointers with each entry
pointing to an input record $\{ r_j \}_{j=1}^{|r|}$.  An excerpt of a
plan is shown in Table~\ref{tbl:example-content-plan}. The order in
the plan corresponds to the sequence in which entities appear in the
game summary. Let $z = z_1 \dots z_{|z|}$ denote the content planning
sequence.  Each $z_k$ points to an input record, i.e.,~$z_k \in \{ r_j
\}_{j=1}^{|r|}$.  Given the input records, the probability $p(z|r)$ is
decomposed as:
\begin{equation}
p(z|r) = \prod_{k=1}^{|z|} {p(z_k | z_{<k},r)} \nonumber
\end{equation}
where $z_{<k} = z_1 \dots z_{k-1}$.

Since the output tokens of the content planning stage correspond to
positions in the input sequence, we make use of Pointer Networks
(Vinyals et al.~\citeyear{NIPS2015_5866}). The latter use attention to
point to the tokens of the input sequence rather than creating a
weighted representation of source encodings. As shown in
Figure~\ref{fig:overall-method}, given $\{ r_j \}_{j=1}^{|r|}$, we use
an LSTM decoder to generate tokens corresponding to positions in the
input.  The first hidden state of the decoder is initialized by $\avg(
\{ \mathbf{r}_j^{cs} \}_{j=1}^{|r|} )$, i.e., the average of record
vectors.  At decoding step~$k$, let $\mathbf{h}_k$ be the hidden state
of the LSTM.  We model $p(z_k=r_j |z_{<k},r)$ as the attention over
input records:
\begin{align}
p(z_k = r_j | z_{<k} , r) \propto \exp (\mathbf{h}_k^\intercal \mathbf{W}_c \mathbf{r}_j^{cs}) \nonumber
\end{align}
where the probability is normalized to $1$, and $\mathbf{W}_c$ are
parameters.  Once $z_k$ points to record~$r_j$, we use the
corresponding vector $\mathbf{r}_j^{cs}$ as the input of the next LSTM
unit in the decoder.

\begin{table}[t]
\small
\begin{center}
\begin{tabular}{|l|l|l|l|}
\hline
\multicolumn{1}{|c|}{\lform{Value}}&\multicolumn{1}{c|}{\lform{Entity}}&\multicolumn{1}{c|}{\lform{Type}}&\multicolumn{1}{c|}{\lform{H/V}}\\
\hline
\lform{Boston}&\lform{Celtics}&\lform{TEAM-CITY}&\lform{V}\\
\lform{Celtics}&\lform{Celtics}&\lform{TEAM-NAME}&\lform{V}\\
\lform{105}&\lform{Celtics}&\lform{TEAM-PTS}&\lform{V}\\
\lform{Indiana}&\lform{Pacers}&\lform{TEAM-CITY}&\lform{H}\\
\lform{Pacers}&\lform{Pacers}&\lform{TEAM-NAME}&\lform{H}\\
\lform{99}&\lform{Pacers}&\lform{TEAM-PTS}&\lform{H}\\
\lform{42}&\lform{Pacers}&\lform{TEAM-FG\_PCT}&\lform{H}\\
\lform{22}&\lform{Pacers}&\lform{TEAM-FG3\_PCT}&\lform{H}\\
\lform{5}&\lform{Celtics}&\lform{TEAM-WIN}&\lform{V}\\
\lform{4}&\lform{Celtics}&\lform{TEAM-LOSS}&\lform{V}\\
\lform{Isaiah}&\lform{Isaiah\_Thomas}&\lform{FIRST\_NAME}&\lform{V}\\
\lform{Thomas}&\lform{Isaiah\_Thomas}&\lform{SECOND\_NAME}&\lform{V}\\
\lform{23}&\lform{Isaiah\_Thomas}&\lform{PTS}&\lform{V}\\
\lform{5}&\lform{Isaiah\_Thomas}&\lform{AST}&\lform{V}\\
\lform{4}&\lform{Isaiah\_Thomas}&\lform{FGM}&\lform{V}\\
\lform{13}&\lform{Isaiah\_Thomas}&\lform{FGA}&\lform{V}\\
\lform{Kelly}&\lform{Kelly\_Olynyk}&\lform{FIRST\_NAME}&\lform{V}\\
\lform{Olynyk}&\lform{Kelly\_Olynyk}&\lform{SECOND\_NAME}&\lform{V}\\
\lform{16}&\lform{Kelly\_Olynyk}&\lform{PTS}&\lform{V}\\
\lform{6}&\lform{Kelly\_Olynyk}&\lform{REB}&\lform{V}\\
\lform{4}&\lform{Kelly\_Olynyk}&\lform{AST}&\lform{V}\\
$\dots$ & $\dots$ & $\dots$ & $\dots$ \\ \hline
\end{tabular}
\end{center}
\caption{Content plan for the example in Table~\ref{fig:example}.}
\label{tbl:example-content-plan}

\end{table}

\subsection{Text Generation}
The probability of output text~$y$ conditioned on content plan~$z$ and
input table~$r$ is modeled as:
\begin{equation}
p(y|r,z) = \prod_{t=1}^{|y|} p(y_t|y_{<t},z,r) \nonumber
\end{equation}
where $y_{<t} = y_1 \dots y_{t-1}$.
We use the encoder-decoder architecture with an attention mechanism to compute $p(y|r,z)$.

We first encode the content plan $z$ into
$\{ \mathbf{e}_k \}_{k=1}^{|z|}$ using a bidirectional LSTM. Because
the content plan is a sequence of input records, we directly feed the
corresponding record vectors $\{ \mathbf{r}_j^{cs} \}_{j=1}^{|r|}$ as
input to the LSTM units, which share the record encoder with the first
stage.

The text decoder is also based on a recurrent neural network with LSTM units.
The decoder is initialized with the hidden states of the final step in the encoder.
At decoding step $t$, the input of the LSTM unit is the embedding of the previously predicted word $y_{t-1}$.
Let $\mathbf{d}_t$ be the hidden state of the $t$-th LSTM unit.
The probability of predicting $y_t$ from the output vocabulary is computed via:
\begin{align}
\beta_{t,k} &\propto \exp (\mathbf{d}_t^\intercal \mathbf{W}_b \mathbf{e}_k) \label{eq:beta:attention} \\
\mathbf{q}_t &= \sum_k \beta_{t,k} \mathbf{e}_k \nonumber \\
\mathbf{d}_t^{att} &= \tanh( \mathbf{W}_d [ \mathbf{d}_t ; \mathbf{q}_t ] ) \nonumber \\
\hspace*{-.7cm}p_{gen} (y_t | y_{<t},z,r) &\hspace*{-.5ex}=\hspace*{-.5ex}\softmax_{y_t}{\hspace*{-.5ex}( \mathbf{W}_y \mathbf{d}_t^{att} + \mathbf{b}_y )}\hspace*{-2ex} \label{eq:prb:gen}
\end{align}
where $\sum_{k} \beta_{t,k} = 1$, $\mathbf{W}_b \in \mathbb{R}^{n \times n} , \mathbf{W}_d \in \mathbb{R}^{n \times 2n} , \mathbf{W}_y \in \mathbb{R}^{n \times |\mathcal{V}_y|} , \mathbf{b}_y \in \mathbb{R}^{|\mathcal{V}_y|}$ are parameters, and $|\mathcal{V}_y|$ is the output vocabulary size.

We further augment the decoder with a copy mechanism, i.e., the ability to
copy words directly from the \emph{value} portions of records in the content plan (i.e., $\{z_{k}\}_{k=1}^{|z|}$).
We experimented with joint \cite{P16-1154} and conditional
copy methods~\cite{P16-1014}.
Specifically, we introduce a variable $u_t \in \{0,1\}$ for each time step to indicate whether the predicted token $y_t$ is copied ($u_t = 1$) or not ($u_t = 0$).
The probability of generating $y_t$ is computed by:
\begin{align}
p(y_t|y_{<t},z,r) = \sum_{u_t \in \{ 0, 1 \}} p(y_t , u_t | y_{<t},z,r) \nonumber
\end{align}
where $u_t$ is marginalized out.

\paragraph{Joint Copy}
The probability of copying from record values and generating from
the vocabulary is globally normalized:
\begin{align}
&p(y_t , u_t | y_{<t},z,r) \propto \nonumber \\
&\begin{cases}
\sum_{y_t \leftarrow z_{k}} \exp (\mathbf{d}_t^\intercal \mathbf{W}_b \mathbf{e}_k) & u_t=1 \\
\exp{( \mathbf{W}_y \mathbf{d}_t^{att} + \mathbf{b}_y )} & u_t=0
\end{cases} \nonumber
\end{align}
where $y_t \leftarrow z_{k}$ indicates that~$y_t$ can be copied from $z_k$, $\mathbf{W}_b$ is shared as in Equation~\eqref{eq:beta:attention}, and $\mathbf{W}_y, \mathbf{b}_y$ are shared as in Equation~\eqref{eq:prb:gen}.

\paragraph{Conditional Copy}
The variable $u_t$ is first computed as a switch gate, and then is used to obtain the output probability:
\begin{align}
&p(u_t = 1 | y_{<t},z,r) = \sigmoid( \mathbf{w}_u \cdot \mathbf{d}_t + b_u ) \nonumber \\
&p(y_t , u_t | y_{<t},z,r) = \nonumber \\
&\begin{cases}
p(u_t|y_{<t},z,r) \sum_{y_t \leftarrow z_{k}} \beta_{t,k} & u_t=1 \\
p(u_t|y_{<t},z,r) p_{gen} (y_t | y_{<t},z,r) & u_t=0
\end{cases} \nonumber
\end{align}
where $\beta_{t,k}$ and $p_{gen} (y_t | y_{<t},z,r)$ are computed as
in~\Crefrange{eq:beta:attention}{eq:prb:gen}, and $\mathbf{w}_u \in
\mathbb{R}^{n}, b_u \in \mathbb{R}$ are parameters.  Following
\citeauthor{P16-1014} \shortcite{P16-1014} and
Wiseman et al.~\shortcite{wiseman2017challenges},
if $y_t$ appears in the content plan during training, we assume that
$y_t$~is copied (i.e., $u_t = 1$).\footnote{Following previous work
  (Gulcehre et al.~\citeyear{P16-1014}; Wiseman et
  al.~\citeyear{wiseman2017challenges}) we learn whether $y_t$ can be
  copied from candidate~$z_{k}$ by applying supervision during
  training. Specifically, we retain~$z_k$ when the record
  entity and its value occur in same sentence in~$y$.}

\subsection{Training and Inference}
Our model is trained to maximize the log-likelihood of the
gold\footnote{Strictly speaking, the content plan is not gold since it
  was not created by an expert but is the output of a fairly accurate
  IE system.}  content plan given table records~$r$ \emph{and} the
gold output text given the content plan and table records:
\begin{equation}
\max \sum_{(r, z, y) \in \mathcal{D} }{ \log{p \left( z | r \right)} + \log{p \left( y | r, z \right)} } \nonumber
\end{equation}
where $\mathcal{D}$ represents training examples (input records,
plans, and game summaries). During inference, the output for input $r$
is predicted by:
\begin{align}
\hat{z} &= \argmax_{z'} p(z' | r) \nonumber \\
\hat{y} &= \argmax_{y'} p(y' | r,\hat{z}) \nonumber
\end{align}
where $z'$ and $y'$ represent content plan and output text candidates,
respectively. For each stage, we utilize beam search to approximately
obtain the best results.

\section{Experimental Setup}
\label{sec:experimental-setup}

\paragraph{Data}
We trained and evaluated our model on \textsc{RotoWire} Wiseman et
al.~\shortcite{wiseman2017challenges}, a dataset of basketball game
summaries, paired with corresponding box- and line-score tables. The
summaries are professionally written, relatively well structured and
long (337~words on average). The number of record types is~39, the
average number of records is~628, the vocabulary size is 11.3K~words
and token count is 1.6M. The dataset is ideally suited for
document-scale generation.  We followed the data partitions introduced
in Wiseman et al.~(\citeyear{wiseman2017challenges}): we trained on
3,398 summaries, tested on 728, and used 727~for validation.

\paragraph{Content Plan Extraction}

We extracted content plans from the \textsc{RotoWire} game summaries
following an information extraction (IE) approach. Specifically, we
used the IE system introduced in Wiseman et
al.~\shortcite{wiseman2017challenges} which identifies candidate
entity (i.e.,~player, team, and city) and value (i.e.,~number or
string) pairs that appear in the text, and then predicts the type (aka
relation) of each candidate pair. For instance, in the document in
Table~\ref{fig:example}, the IE system might identify the pair
``\lform{\small Jeff Teague, 20}'' and then predict that that their
relation is ``\lform{\small PTS}'', extracting the record
(\lform{\small Jeff Teague, 20, PTS}). Wiseman et
al.~\shortcite{wiseman2017challenges} train an IE system on
\textsc{RotoWire} by determining word spans which could represent
entities (i.e.,~by matching them against players, teams or cities in
the database) and numbers. They then consider each entity-number pair
in the same sentence, and if there is a record in the database with
matching entities and values, the pair is assigned the corresponding
record type or otherwise given the label ``none'' to indicate
unrelated pairs.

We adopted their IE system architecture which predicts relations by
ensembling 3 convolutional models and 3 bidirectional LSTM models. We
trained this system on the training portion of the \textsc{RotoWire}
corpus.\footnote{A bug in the code of Wiseman et
  al.~\shortcite{wiseman2017challenges} excluded number words
  from the output summary. We corrected the bug and this resulted in
  greater recall for the relations extracted from the summaries. See
  the supplementary material for more details.}  On held-out data it
achieved 94\% accuracy, and recalled approximately 80\% of the
relations licensed by the records. Given the output of the IE system,
a content plan simply consists of (entity, value, record type, h/v)
tuples in their order of appearance in a game summary (the content
plan for the summary in Table~\ref{fig:example} is shown in
Table~\ref{tbl:example-content-plan}).  Player names are pre-processed
to indicate the individual's first name and surname (see \lform{\small
  Isaiah} and \lform{\small Thomas} in
Table~\ref{tbl:example-content-plan}); team records are also
pre-processed to indicate the name of team's city and the team itself
(see \lform{\small Boston} and \lform{\small Celtics} in
Table~\ref{tbl:example-content-plan}).

\paragraph{Training Configuration}
We validated model hyperparameters on the development set. %
We did not tune the dimensions of word embeddings and LSTM hidden
layers; we used the same value of ~$600$ reported in Wiseman et
al.~\shortcite{wiseman2017challenges}.  We used one-layer pointer
networks during content planning, and two-layer LSTMs during text
generation.  Input feeding (Luong et al.~\citeyear{D15-1166}) was
employed for the text decoder.  We applied dropout~(Zaremba et
al.~\citeyear{zaremba2014recurrent}) at a rate of~$0.3$. Models
were trained for $25$~epochs with the Adagrad optimizer~(Duchi et
al.~\citeyear{duchi2011adaptive}); the initial learning rate
was~$0.15$, learning rate decay was selected from $\{0.5, 0.97\}$, and
batch size was~5. For text decoding, we made use of
BPTT~\cite{mikolov2010recurrent} and set the truncation size to~$100$.
We set the beam size to~$5$ during inference.  All models are
implemented in OpenNMT-py \cite{klein2017opennmt}.

\section{Results}
\label{sec:results}

\paragraph{Automatic Evaluation}
We evaluated model output using the metrics defined in Wiseman et
al. \shortcite{wiseman2017challenges}.  The idea is to employ a fairly
accurate IE system (see the description in
Section~\ref{sec:experimental-setup}) on the gold and automatic
summaries and compare whether the identified relations align or
diverge.

Let $\hat{y}$~be the gold output, and $y$~the system output.
\emph{Content selection} (CS) measures how well (in terms of precision
and recall) the records extracted from $y$~match those found
in~$\hat{y}$.  \emph{Relation generation} (RG) measures the factuality
of the generation system as the proportion of records extracted
from~$y$ which are also found in $r$ (in terms of precision and number
of unique relations). \emph{Content ordering} (CO) measures how well
the system orders the records it has chosen and is computed as the
normalized Damerau-Levenshtein Distance between the sequence of
records extracted from $y$~and~$\hat{y}$. In addition to these
metrics, we report BLEU \cite{papineni-EtAl:2002:ACL}, with
human-written game summaries as reference.

Our results on the development set are summarized in
Table~\ref{generation-from-plan-dev}. We compare our Neural Content
Planning model (NCP for short) against the two encoder-decoder (ED)
models presented in Wiseman et al. \shortcite{wiseman2017challenges}
with joint copy (JC) and conditional copy (CC), respectively. In
addition to our own re-implementation of these models, we include the
best scores reported in Wiseman et
al.~\shortcite{wiseman2017challenges} which were obtained with an
encoder-decoder model enhanced with conditional copy.
Table~\ref{generation-from-plan-dev} also shows results when NCP uses
oracle content plans (OR) as input. In addition, we report the
performance of a template-based generator (Wiseman et
al. \citeyear{wiseman2017challenges}) which creates a document
consisting of eight template sentences: an introductory sentence (who
won/lost), six player-specific sentences (based on the six
highest-scoring players in the game), and a conclusion sentence.

\begin{table}[t]
\small
\centering
\begin{tabular}{@{~}l@{~}|@{~}c@{~~~}c@{~}|c@{~~~}c|c|@{~}c@{~} }
 \thickhline
 \multirow{2}{*}{Model} &\multicolumn{2}{c|}{RG} &\multicolumn{2}{c|}{CS} & CO & \multirow{2}{*}{BLEU}\\

 &\# & P\% & P\% & R\% & DLD\% & \\ \thickhline
TEMPL &54.29 &99.92 &26.61 &59.16 &14.42 &8.51  \\
WS-2017 & 23.95 & 75.10 & 28.11 & 35.86 & 15.33 & 14.57 \\
ED+JC & 22.98 & 76.07 & 27.70 & 33.29 & 14.36 & 13.22  \\ %
ED+CC & 21.94 & 75.08 &27.96 & 32.71 & 15.03 & 13.31 \\ %
NCP+JC  & 33.37 & 87.40 & 32.20 & 48.56 & 17.98 & 14.92 \\ %
NCP+CC &{33.88} & {87.51} & {33.52} & {51.21}
 &{18.57} & {16.19}\\ %
NCP+OR & 21.59 & 89.21 & 88.52 & 85.84 & 78.51 & 24.11\\
\thickhline
\end{tabular}
\caption{\label{generation-from-plan-dev} Automatic evaluation
  on  \textsc{RotoWire} development set using
  relation generation (RG)  count (\#) and precision (P\%), content
  selection (CS) precision (P\%) and recall (R\%), content ordering (CO) in normalized
  Damerau-Levenshtein distance (DLD\%), and BLEU.}%
\end{table}

\begin{table}[t]
\small
\centering
\begin{tabular}{ @{~}l@{~}|@{~}c@{~~}c|c@{~~}c|c|c@{~} }
 \thickhline
\multirow{2}{*}{Model} &\multicolumn{2}{c|}{RG} &\multicolumn{2}{c|}{CS} & CO & \multirow{2}{*}{BLEU}\\

 &\# & P\% & P\% & R\% & DLD\% & \\
\thickhline
ED+CC & 21.94 & 75.08 &27.96 & 32.71 & 15.03 & 13.31 \\ %
CS+CC &24.93 &80.55 &28.63 &35.23 &15.12 &13.52 \\ %
CP+CC &33.73 &84.85 &29.57 &44.72 &15.84 &14.45 \\ %
NCP+CC &{33.88} & {87.51} & {33.52} & {51.21}
 &{18.57} & {16.19}\\ %
NCP & 34.46 & ---& 38.00 & 53.72 & 20.27& --- \\
\thickhline
\end{tabular}
\caption{\label{tbl:ablation-content-selection-planning}Ablation
  results on  \textsc{RotoWire} development set using
  relation generation (RG)  count (\#) and precision (P\%), content
  selection (CS) precision (P\%) and recall (R\%), content ordering
  (CO) in normalized Damerau-Levenshtein distance (DLD\%),
  and BLEU.}
\end{table}

As can be seen, NCP improves upon vanilla encoder-decoder models
(ED+JC, ED+CC), irrespective of the copy mechanism being employed. In
fact, NCP achieves comparable scores with either joint or conditional
copy mechanism which indicates that it is the content planner which
brings performance improvements. Overall, NCP+CC achieves best content
selection and content ordering scores in terms of BLEU. Compared to
the best reported system in Wiseman et
al. \shortcite{wiseman2017challenges}, we achieve an absolute
improvement of approximately 12\% in terms of relation generation;
content selection precision also improves by~5\% and recall by~15\%,
content ordering increases by 3\%, and BLEU by 1.5~points. The results
of the oracle system (NCP+OR) show that content selection and ordering
do indeed correlate with the quality of the content plan and that any
improvements in our planning component would result in better
output. As far as the template-based system is concerned, we observe
that it obtains low BLEU and CS precision but scores high on CS recall
and RG metrics.  This is not surprising as the template system is
provided with domain knowledge which our model does not have, and thus
represents an upper-bound on content selection and relation
generation. We also measured the degree to which the game summaries
generated by our model contain redundant information as the proportion
of non-duplicate records extracted from the summary by the IE system.
84.5\% of the records in NCP+CC are non-duplicates compared to Wiseman
et al.  \shortcite{wiseman2017challenges} who obtain 72.9\% showing
that our model is less repetitive.

\begin{table}[t]
\small
\centering
\begin{tabular}{@{~}l@{~}|@{~}c@{~~~}c@{~}|c@{~~~}c|c|@{~}c@{~} }
 \thickhline
 \multirow{2}{*}{Model} &\multicolumn{2}{c|}{RG} &\multicolumn{2}{c|}{CS} & CO & \multirow{2}{*}{BLEU}\\
 &\# & P\% & P\% & R\% & DLD\% & \\ \thickhline
TEMPL &\textbf{54.23} &\textbf{99.94} &26.99 &\textbf{58.16} &14.92 &\hspace*{1ex}8.46  \\
WS-2017 & 23.72 & 74.80 & 29.49 & 36.18 & 15.42 & 14.19 \\%\hline
NCP+JC & 34.09 & 87.19 & 32.02 & 47.29 & 17.15 & 14.89 \\ %
NCP+CC &{34.28} & {87.47} & \textbf{34.18} & {51.22}
 &\textbf{18.58} & \textbf{16.50}\\ %
\thickhline
\end{tabular}
\caption{\label{generation-from-plan-test} Automatic evaluation
  on  \textsc{RotoWire} test set using   relation generation (RG)
  count (\#) and precision (P\%), content selection (CS) precision
  (R\%) and recall (R\%), content ordering (CO) in normalized
  Damerau-Levenshtein distance (DLD\%), and BLEU.}
\end{table}

We further conducted an ablation study with the conditional copy
variant of our model (NCP+CC) to establish whether improvements
are due to better content selection (CS) and/or content planning (CP).
We see in Table~\ref{tbl:ablation-content-selection-planning} that
content selection and planning individually contribute to performance
improvements over the baseline (ED+CC), and accuracy further increases
when both components are taken into account.  In addition we evaluated
these components on their own (independently of text generation) by
comparing the output of the planner (see $p(z|r)$ block in
Figure~\ref{fig:overall-method}) against gold content plans obtained
using the IE system (see row NCP in
Table~\ref{tbl:ablation-content-selection-planning}.  Compared to the
full system (NCP+CC), content selection precision and recall are
higher (by~4.5\% and 2\%, respectively) as well as content ordering
(by~1.8\%). In another study, we used the CS and CO metrics to measure
how well the generated text follows the content plan produced by the
planner (instead of arbitrarily adding or removing information). We
found out that NCP+CC generates game summaries which follow the
content plan closely: CS precision is higher than 85\%, CS recall is
higher than 93\%, and CO higher than 84\%. This reinforces our claim
that higher accuracies in the content selection and planning phases
will result in further improvements in text generation.

The test set results in Table~\ref{generation-from-plan-test} follow a
pattern similar to the development set.  NCP achieves higher accuracy
in all metrics including relation generation, content selection,
content ordering, and BLEU compared to Wiseman et
al.~\shortcite{wiseman2017challenges}. We provide examples of system
output in Table~\ref{tab:output} and the supplementary material.

\begin{table}[t]
\scriptsize
\centering
\begin{tabular}{@{~}p{8.3cm}@{~}} \thickhline
{\color{blue}The Golden State Warriors (10--2) defeated the
  Boston Celtics (6--6) 104--88. Klay Thompson scored 28 points
  (12--21 FG, 3--6 3PT, 1--1 FT) to go with 4 rebounds. Kevin Durant
  scored 23 points (10--13 FG, 1--2 3PT, 2--4 FT) to go with 10
  rebounds. Isaiah Thomas scored 18 points (4--12 FG, 1--6 3PT, 9--9
  FT) to go with 2 rebounds. Avery Bradley scored 17 points (7--15 FG,
  2--4 3PT, 1--2 FT) to go with 10 rebounds. Stephen Curry  scored 16
  points (7--20 FG, 2--10 3PT, 0--0 FT) to go with 3  rebounds. Terry
  Rozier scored 11 points (3--5 FG, 2--3 3PT, 3--4 FT)  to go with 7
  rebounds.} The Golden State Warriors' next game will be at home
against the Dallas Mavericks, while the Boston Celtics will travel to
play the Bulls. \\ \hline
{\color{blue}The Golden State Warriors defeated the Boston Celtics 104--88 }at TD Garden on Friday. {\color{blue}The Warriors (10--2)}
came into this game winners of five of their last six games, but the
{\color{red}Warriors (6--6)} were able to pull away in the second half. {\color{blue}Klay Thompson led the way for the Warriors with 28 points on 12--of--21 shooting, while Kevin Durant added 23 points, 10 rebounds, seven assists and two steals. Stephen Curry added 16 points and eight assists, while Draymond Green rounded out the box score with 11 points, eight rebounds and eight assists. For the Celtics, it was Isaiah Thomas who shot just 4--of--12 from the field and finished with 18 points. Avery Bradley added 17 points and 10 rebounds}, while the rest of the Celtics {\color{red}combined to score just seven points}. Boston will look to get back on track as they play host to the 76ers on Friday. \\
\thickhline
\end{tabular}
\caption{\label{tab:output} Example output from TEMPL (top)
  and NPC+CC (bottom). Text that accurately reflects
  a record in the associated box or line score is in {\color{blue}blue}, erroneous text
  is in {\color{red}red.}}
\end{table}

\paragraph{Human-Based Evaluation}
We conducted two human evaluation experiments using the Amazon
Mechanical Turk (AMT) crowdsourcing platform. The first study assessed
relation generation by examining whether improvements in relation
generation attested by automatic evaluation metrics are indeed
corroborated by human judgments.  We compared our best performing
model (NCP+CC), with gold reference summaries, a template system and
the best model of Wiseman et
al. \shortcite{wiseman2017challenges}. AMT workers were presented with
a specific NBA game's box score and line score, and four (randomly
selected) sentences from the summary. They were asked to identify
supporting and contradicting facts mentioned in each sentence. We
randomly selected 30~games from the test set. Each sentence was rated
by three workers.

The left two columns in Table~\ref{tbl:human-eval} contain the average
number of supporting and contradicting facts per sentence as
determined by the crowdworkers, for each model.  The template-based
system has the highest number of supporting facts, even compared to
the human gold standard. TEMPL does not perform any content selection,
it includes a large number of facts from the database and since it
does not perform any generation either, it exhibits a few
contradictions.  Compared to WS-2017 and the Gold summaries, NCP+CC
displays a larger number of supporting facts. All models are
significantly\footnote{All significance differences reported
  throughout this paper are with a level less than 0.05.}  different
in the number of supporting facts (\#Supp) from TEMPL (using a one-way
ANOVA with post-hoc Tukey HSD tests). NCP+CC is significantly
different from WS-2017 and Gold.  With respect to contradicting facts
(\#Cont), Gold and TEMPL are not significantly different from each
other but are significantly different from the neural systems
(WS-2017, NCP+CC). %

In the second experiment, we assessed the generation quality of our
model.  We elicited judgments for the same 30~games used in the first
study. For each game, participants were asked to compare a
human-written summary, NCP with conditional copy (NCP+CC), Wiseman et
al.'s \shortcite{wiseman2017challenges} best model, and the template
system.
Our study used \textit{Best-Worst Scaling} (BWS;
\citeauthor{louviere2015best} \citeyear{louviere2015best}), a technique shown to be
less labor-intensive and providing more reliable results as compared to
rating scales \cite{kiritchenko2017best}. We arranged every 4-tuple of competing
summaries into 6~pairs. Every pair was shown to three crowdworkers,
who were asked to choose which summary was {\it best} and which was {\it worst}
according to three criteria: \emph{Grammaticality}
(is the summary fluent and grammatical?), \emph{Coherence} (is the
summary easy to read? does it follow a natural ordering of facts?),
and \emph{Conciseness} (does the summary avoid redundant information
and repetitions?). The score of a system for each criterion is computed as the difference between the percentage
of times the system was selected as the best and the percentage of times it was
selected as the worst \cite{orme2009maxdiff}. The scores range from $-100$
(absolutely worst) to $+100$ (absolutely best).

\begin{table}[t]
\small
\centering
\begin{tabular}{@{~}lccrrr@{~}}
\thickhline
 & \#Support & \#Contra & Gram & Cohere & Concise \\
\thickhline
Gold    & 2.98 & 0.28 & 11.78 & 16.00& 13.78 \\
TEMPL   & {6.98} & {0.21} & -0.89 &-4.89 & 1.33\\
WS-2017 & 3.19 & 1.09 & -4.22 &-4.89 &-6.44 \\
NCP+CC  & 4.90 & 0.90 & -2.44 & -2.44&-3.55\\
\thickhline
\end{tabular}
\caption{Average number of supporting (\#Support) and contradicting
  (\#Contra) facts
  in game summaries and   \textit{best-worst scaling}
  evaluation (higher is better) for grammaticality (Gram), Coherence (Cohere), and
  Conciseness (Concise).}
\label{tbl:human-eval}
\end{table}

The results of the second study are summarized in
Table~\ref{tbl:human-eval}.  Gold summaries were perceived as
significantly better compared to the automatic systems across all
criteria (again using a one-way ANOVA with post-hoc Tukey HSD
tests). NCP+CC was perceived as significantly more grammatical than
WS-2017 but not compared to TEMPL which does not suffer from
fluency errors since it does not perform any generation. NCP+CC was
perceived as significantly more coherent than TEMPL and WS-2017. The
template fairs poorly on coherence, its output is stilted and exhibits
no variability (see top block in Table~\ref{tab:output}). With regard
to conciseness, the neural systems are significantly worse than TEMPL,
while NCP+CC is significantly better than WS-2017. By design the
template cannot repeat information since there is no redundancy in the
sentences chosen to verbalize the summary.

Taken together, our results show that content planning improves
data-to-text generation across metrics and systems. We find that
NCP+CC overall performs best, however there is a significant gap
between automatically generated summaries and human-authored ones.

\section{Conclusions}
\label{sec:conclusions}

We presented a data-to-text generation model which is enhanced with
content selection and planning modules. Experimental results (based on
automatic metrics and judgment elicitation studies) demonstrate that
generation quality improves both in terms of the number of relevant
facts contained in the output text, and the order according to which
these are presented. Positive side-effects of content planning are
additional improvements in the grammaticality, and conciseness of the
generated text.  In the future, we would like to learn more
detail-oriented plans involving inference over multiple facts and
entities. We would also like to verify our approach across domains and
languages.

\fontsize{9.0pt}{10.0pt} \selectfont

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Imam Al-Busiri (Abu 'Abdallah Muhammad ibn Sa'íd ul-Busiri Ash Shadhili) (1211–1294) was a Sufi Egyptian poet, originally from Moroccan Sanhaji Berber descent belonging to the Shadhiliyya order. He lived in Egypt, where he wrote under the patronage of Ibn Hinna, the vizier. The most famous of these is the Qasida al-Burda (Poem of the Mantle). It is entirely in praise of the Islamic prophet, Muhammad, who cured the poet of paralysis by appearing to him in a dream and wrapping him in a mantle. The poem has had a unique history (cf. I. Goldziher in Revue de l'histoire des religions, vol. xxxi. pp. 304 ff.). Even in the poet's lifetime, it was regarded as sacred. Up to the present time its verses are used as amulets; it is employed in the lamentations for the dead; it has been frequently edited and made the basis for other poems, and new poems have been made by interpolating four or six lines after each line of the original. It has been published with English translation by Faizullabhai (Bombay, 1893), with French translation by R. Basset (Paris, 1894), with German translation by C. A. Ralfs (Vienna, 1860), and in other languages elsewhere.
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Entertainment Movie Reviews 02 Mar 2019 Luka Chuppi movie review: A poorly executed live-in satire that doesn't take off
Entertainment, Movie Reviews
Luka Chuppi movie review: A poorly executed live-in satire that doesn't take off
THE ASIAN AGE. | ARNAB BANERJEE
Published : Mar 2, 2019, 3:36 am IST
Updated : Mar 2, 2019, 3:36 am IST
Laxman Utekar's debut film has an interesting premise, but it isn't that film you have been waiting for:
The writer is unable to impose rhythm on the escalating complications, and often allows a scene to go on far too long, without any solid material.
Cast: Kartik Aaryan, Kriti Sanon, Pankaj Tripathi, Vinay Pathak, Alka Amin
Director: Laxman Utekar
The romantic comedy Luka Chhupi functions as a guidebook of sorts to the somewhat hesitant couple who have just fallen for each other, but coming from the small town of Mathura, they look rather unsure of whether to tie the knot or not — especially the girl, who demands more time to weigh the situation before making a decision. She has reservations about the boy — or whether she really likes him-or not — and suggests that they go in for a live-in relationship!
Laxman Utekar's debut film has an interesting premise, but it isn't that film you have been waiting for: The modern take on the relationship between a boy and a girl who are unmarried, and would rather stay together without engaging into a legally binding association.
Set in Mathura, it merely gives us a bird's view of how small towns in North India enjoy all the accoutrements of the modern world, and yet, when it comes to the age-old mindset ingrained in antiquated values, the local residents have a long way to go. I am not too sure if the Indian middle-class is still regressively outdated; or that it would take another century for them to come to terms with the 20th century's rapid changes as we have seen in the last decade or so. Many characters in the film continue to be non-conformist in their outlook, and are in fact oddities as contemporary parents or individuals in this day and age.
As the mast maula, Gudu Shukla (Aaryan), we have a hero, who, in his own right, is the star reporter of a Mathura cable news channel. His cameraman Abbas (Aparshakti Khurana) is someone Guddu counts on, both as a team out to cover news locally, as well as, for his own personal comfort and support. Guddu's parents Badriprasad Shukla (Atul Srivastava) and Shakuntala (Alka Amin) continue to live a life full of antiquated values that is reminiscent of the 1960's.
Guddu's brother-in-law Babulal (Pankaj Tripathi) is someone who does nothing for a living and spends all his time lustily chasing married women and whiling away time. Guddu falls for his intern, the headstrong Rashmi Trivedi (Kriti Sanon), who has just landed from the capital city of New Delhi, and is keen to learn the ropes of television journalism before branching out to greener pastures.
Meanwhile, all hell breaks loose as one of Bollywood's most sought-after stars Nazeem Khan is hounded by the media and slammed for his live-in relationship. Quite obviously, no one likes such bold-faced cheeky behaviour of a role model.
The self-righteous custodians of morality — principal among them being Rashmi's father Vishnu Prasad Trivedi (Vinay Pathak) of the Sanskriti Raksha Manch — have an even larger issue: They wouldn't approve of such an unacceptable act by someone who belongs to the minority community. Fortunately for Guddu, it is Rashmi who has no reservations about not conforming to social customs, and is gung-ho right about moving into a place with him.
What follows next is routine: The social hindrances loom large to ruin their romance and Guddu's family discovers his luka chhupi (hide and seek) union, and threaten them with dire consequences. Guddu's parents throw a fit and the couple decides to formalise their bond in the presence of their respective parents and of course, a priest. Not that it's a smooth run thereafter; several familial characters and sudden quirks of the couple derail the proceedings while intermittently giving us hope of a few bright spots here and there, only to degenerate into nothingness.
Director Utekar and writer Rohan Shankar had probably come across a rigorously even-handed idea about how people in small towns and cities are still grappling with the fast evolving notions of marriage, cohabitation and old rituals. That they are unimaginative begins to show right after the first half when clichéd situations never allow the film to take off. All the tumult that follows looks premeditated and doesn't mesh together with the incompatible viewpoints and motivations of the old and the young. The original premise could have been developed into something of great measure, like the new generation gradually controlling the outdated values and having its way. Funnily enough, it's the women (Rashmi; Guddu's mother; another old woman on the street) who seem to be far more progressive in their views and are seen welcoming the freshness of the bold departure from the traditional. Lazy writing makes it less witty and enrapturing. The madly-in-love couple in the throes of amorous passion even try to maintain their liaison as they descend deeper and deeper into the insular world of domestic nitty-gritties. After the emotional upheaval of all the loads they are forced to endure, they do poke fun at the institution of marriage. Too bad, it is perilously close to losing appeal even as it tries hard to expose the foibles of conventional relationships.
The writer is unable to impose rhythm on the escalating complications, and often allows a scene to go on far too long, without any solid material. The drama strains to achieve characters' conflicts convincingly, stretching between breezy satire and convention mores .The result is, at times, a skilfully rehearsed script reading rather than unexpected turn-of-life events.
Aaryan does whatever he can to step into the shoes of the current crop of successful heroes: Rajkummar Rao, Shahid Kapoor, Ranveer Singh, Ranbir Kapoor, to name a few. Is this the film that could catapult him to their level? Nah, he needs a lot more to blend chutzpah and emotive range to pull off comedy. For Sanon too, this may not be the ideal vehicle to allow her to go up the ladder, but it's her natural charm that has an instant appeal. Scene-stealers Tripathy and Khurana have the right skills to give us a few laughs but the poor execution of this mostly unamusing two-hour comedy makes it an opportunity lost!
Tags: luka chuppi, kartik aaryan, luka chuppi movie review


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Ukraine: Day of Mourning at Verkhovna Rada (Supreme Council) of Ukraine
Posted on December 3, 2010 by Serbian Animals Voice (SAV)
OriginaL: http://maidan.org.ua/static/news/2010/1286123678.html
http://www.kontrast.org.ua/news/1199.html
Day of mourning at Verkhovna Rada (Supreme Council) of Ukraine
Tatiana Metelova
October 4-5 animal protectors in Ukraine will conduct the first national event to mark the International Day of Protection of Animals. International Day for the Protection of Animals was established in 1931 to honor the memory of Francis of Assisi – the patron of all life. Since then this day is celebrated in all European countries. This year in England, for example, during the day will worship in churches for health, prosperity and peace for all living things.
In Ukraine this day animal protectors declared day of mourning for those killed and tortured human friends. It's no secret that the mass killings of homeless animals (also to those ranks often get quite homely ones, thrown out by their masters, especially newborns) with the upcoming Euro 2012 turned our country into a brutal and horrible routine.
Pictures of Oswiecim and Buhenwald crematoriums is still too fresh in european memory for viewing nowadays the same picture they observe in Ukraine: mobile furnaces where 4-paw human friends are burned alive, which are driving in the streets of Mariupol and Lugansk and used at full capacity under the Kuiv in Borodianka shelter.
In October 2007 the Kiev City Council, heading in a civilized way, adopted new Rules for keeping dogs and cats in Kiev, and in December of that year – the Program of reducing the number of stray animals by humane methods. The program included capturing and mass sterilization of stray animals with their subsequent returning to place of their original residence (the CNR method – catch/neuter/return). It was provided by the Rules to establish control over breeding of animals. For example, Paragraph 3.11 of the Rules obligated owners of dogs and cats to adjust their animals offspring by sterilization of those who have no breeding value, and Paragraph 3.12 forbided owners to engage in breeding … if they could not keep and settle an offspring.
Thus, cumulatively, Program and Rules were aimed to "overlap" both sources of replenishing number of homeless animals: their natural reproduction (the program was based on getting rid them of their reproductive capacity) and replenishment by uncontrolled reproduction of proprietary animal and their subsequent ejection into the street.
Analysis of the European experience of control of the number of homeless dogs ( http://www.kontrast.org.ua/news/1183.html ) shows that the model chosen by the Kiev government regulation (CNR) is widely used in European countries, based on consistent humane principles and has significant advantages over other models, in particular, requires not more funding than the other models (capture without return). In all countries where it is used at the national or regional level, the situation of homeless animals is controlled, and their numbers at least do not increase. Instead, countries where killing is the way of regulation , the number of homeless animals is continuously growing. Practice of killing is common exclusively in some post-communist countries, mostly with the strong influence of Muslim traditions (Albania, Armenia). In the same group Moldova and Ukraine are also appeared . Thus, as experience shows, cruelty and barbarism found also economically irrational and reduced to not only loss of the positive image of Ukraine in Europe, but also to vain and fruitless expenditure of public funds.
However, the maximum efficiency of method of regulation chosen by Kyiv is achieved only by joint efforts of civic animalprotect organizations with work of state and local governments toward preventing replenishment contingent of homeless animals due to uncontrolled breeding of property. There are following means of such prevention:
· Obligatory registration of proprietary dogs;
· Preferential proprietary sterilization of animals for vulnerable groups of people (with low income – such as pensioners, invalids, etc.);
· Educational work with the population (social advertising, publishing, television and radio, lecture work in schools, etc.)
Those countries reached the greatest success in regulating the number of homeless animals by CNR where state and / or local authorities has been involved into implementation of means of prevention of uncontrolled breeding of proprietary animals.
Adopted by Kyiv city authorities (as well as Kharkov, Odessa and several other cities) program included some of these measures. In practice, however its implementation was disrupted: there was neither mass capture and sterilization, nor creation of the mini-shelters for homeless animals for staying during sterilization nor, above all, the introduction of birth control animals that have got their owners, nor conducting information and educational work among the population that would contribute to recovering the main sources of increasing stray animals.
Over the period of 2008 – early 2010 implementation and funding of the Programme took place mostly by volunteers – Kyiv residents who spend their life time and their own money to capture, sterilization, postoperative support-stationar staying after sterelization / neutering, vaccination against zoonotic (transmitted to man) diseases stray animals and taking measures to adoptation in new families. However, if in 2008 – in early 2009, the city government not at least put up resistance to these measures, providing an partial opportunity for free sterilization and, for although in very limited volume, but postoperative stationar (in communal vetclinic at Troeschina district,- author's notes). But since 2009 the situation changed at full opposite – public organizations were completely excluded from participation in the Program and control over this process: it was stopped informing public organizations about the date and place of captures, public organizations representatives were denied visiting Communal utility "Shelter for animals" Borodyanka" and denied the opportunity to take animals under their care, stopped free sterilization and free postoperational stay in municipal veterinary institutions.
As a result of non-fulfillment and deliberate sabotage of Programme, due to already sterilized and healthy, vaccinated animals that were under the care of volunteers were DESTROYED, aggressive, ailing, unvaccinated and unsterilized, which continue to breed.came on their place in the suburbs. Lack of proper registration of proprietary animal, lack of measures to encourage their sterilization, led to the next replenishment of homeless animals with those that were thrown out by uncontrolled breeders. As a result of these processes the number of homeless animals in the city, despite all the efforts of volunteers, not supported by local authorities has increased to the critical point. And the reason is not realization of the Program but its sabotage!
More worse situation is in other cities of Ukraine. In most of them local government even do not want to cooperate and ignores not only offers of animal protectors, but also a LAW OF UKRAINE No. 3447 – IV On the Protection of Animals from Cruelty, adopted in February 2006.
In recent years animal protectors of Ukraine tried to achieve a civilized solution of this problem at the local government by both – work and conducting confrontation and picket at the walls of City Hall. With the increasing negative international reaction to the actions of Ukrainian local authorities, especially after August 30 when animal protectros picketed the arrival of the President Janukovich in Berlin" and The Cabinet of Ministers of Ukraine received a letters, warning of boycott Euro 2012 in Ukraine, some changes seemed to be started.
What's more: at the same meeting animal carers heard that a significant part of questions related to the problem can not and will not be solved by local authorities. And some of these statements are making sense. Local authorities indeed can not jump over his own head and overcome those obstacles on the way to solving the problems which inherented in current regulatory framework. And in most cases government uses such interferences to do nothing. This applies in particular to the already mentioned measures of birth control of owners animals taxes, penalties, local taxes, duties and privileges, which could be to encourage pet owners to sterilize their pets, those that do not have no breeding value. And in conditions of continuous replenishment of homeless animals due to thrown away owner's pets any attempts to solve the problem are doomed to failure.
In Kiev animalcare organizations were invited to a meeting with heads of district councils, representatives of central government – Ministry of Environmental Protection and the State Department of Veterinary Medicine. The meeting, held September 8th 2010, chaired by A. Golubchenko – Deputy Mayor, seemed to be constructive. Animal protectors were invited to make proposals for solving the problem of reducing the number of homeless animals in the city and it was promised to discuss them and reach mutual result at the next joint meeting which was planned to held in 10 days. Proposals have been made by officially – during picketing KSCA Sept. 10. However, 10 days passed by a long time ago, but promises, as always, remain only promises.
Besides, there are too much ambiguities and contradictions in the operating (rather called – "inoperative") Law on animal protection … – so much that it can be used by anybody as he pleases. For example, in Article 1, where concepts used in the law are defined, only 4 categories of animals are named: wild, domestic, agricultural and homeless. And the homeless ones called "domestic animals abandoned by humans or living in semi-free groups, able to reproduce outside man's control". In the same time in Article 17, which defines cases in which killing of animals is permitted, another category not previously mentioned is appeared – "animals not kept by man but existing in conditions wholly or partly caused by human activity". Usually rodents is called in this way – rats, mice. etc. However, using this dressing action of the "murderous" article applies to homeless animals – those which left unattended, although there is any words mentioned about them in Article 17.
Another appellation, nowhere else in the law is not used, appears in Article 24 – "stray dogs". And what these ones are? Law doesn't define them. Do they differ from the homeless and how to distinguish which of them are attributes? And if they do not differ, why various terms for quarantine and follow different destinies are defined for "homeless" and "stray". Homeless ones "may be returned to owners", and stray – only transferred to the shelter. Here is your cat jumped out of the door and run the world behind. You are looking for it. And the service of capture catches her and … recognizes as not homeless, but stray. On what basis? However, if it named stray – according to letter of the law, it can not be returned to you. Interesting situation, is not it?
Thus, the law requires immediate correction. Without it we can not avoid its different understanding in the different cities of Ukraine and by various officials.
Nevertheless, the Bill of the new wording of the Law -designed by animalprotectors and unofficially passed to the Supreme Council of Ukraine (Parliament) for the second year still be here. However, during the working on the law no one wants to take into consideration changes which was proposed in the mentioned Bill. Instead, in December 2009 Parliament has abolished licensing for breeding animals. «Be fruitful and multiply!», animals, and using your offspring will be easy to launder budget funds allocated for murder!
In February 2010 amendments to the law which were adopted which, among really good innovations, canceled responsibility for abuse on domestic and homeless animals at all. In particular, Article 35 identified cases involving the liability. Here is the text of this article in the new proposed wording:
"Article 35. Responsibility for violation of legislation protecting animals from cruelty
Responsibility for violation of legislation protecting animals from cruelty are natural and legal persons guilty of:
– Violating the anti-epizootic, veterinary and sanitary, quarantine rules and regulations;
– Abuse of the animals during the entertainment or sports events, which include fighting animals or their elements, and other events that cause suffering, injury, bodily injury or death of animals;
– non-fulfillment of legitimate requirements of control authorities , that carry out control on protecting animals from cruelty.
– Violation of legislation protecting animals from cruelty entails administrative, civil or criminal liability under law.
– Responsibility for other violations of legislation protecting animals from cruelty may be set by present Law
Individuals and legal persons are obliged to compensate damage caused to them due to violation of legislation for protecting animals from abuse, in the manner and amounts set by law. "
Apparently, only three cases involving the liability:
– Violation of sanitary regulations, violations of the treatment of animals during entertainment activities and non-compliance with the requirements, of local authorities that make control on matters relating to animals.
So, responsible for cruelty to domestic and stray animals are not provided.
In practice it means that, torment, murder, torture – cutting off the legs and scratching out eyes, burning alive and hung on trees will be possible to do without punishment. Instead, keep pets at home becomes problematic: the rescuers, who in their own funds treat homeless animals, always could be blamed, for example, in anti-sanitation (which is just forbidden by the law). And what is more: local authorities may make decisions with impunity about the massacres, even for example, force removal of pets from owners. But those who will protest against the arbitrary power and try to protect their own pets, will become violators of the law (see paragraph 3) and subjects to punishment.
Fortunately, President Yushchenko who was in force in that time, vetoed the law albeit with a ridiculous reason, therefore, the Law has preserved the validity of the old version. However, according to lobby information from the Parliament, lawmakers plan to return to see it again and can adopt this harmful version now, in October 2010.
To prevent changes in the law for the worse, to attract attention of deputies to the possibilities of a civilized way that gives us the European experience and which are incorporated in the project developed by animalprotectors, they decided to celebrate the day of mourning for the innocent victims by mass "march on Parliament" – conduct rally-requiem for the dead under the walls of the legislature.
Action will begin 4 th at 6 p.m. in the evening , when in the central squares of cities in Ukraine will burn candles of mourning. People would just stand with the photos of their killed animals and will remember them. But at the 5 th at 12 o'clock animal protectors of Ukraine must go to the centers of local authorities and support with their requirements their capital colleagues who will express their proposals to lawmakers. Among these requirements is not only an immediate end of killings, but also:
— Real, not fictional criminal responsibility for cruelty and murder;
— Tax penalties for reproduction and sale breed animals for profit;
— Penalties for uncontrolled breeding and throwing animals into the street animals;
— Licensing of animal breeding
— Legal legalization of carers for homeless animals and program CNR (capture-neuter-return) in the scale of the whole country.
Within the bounds of Action performance-requiem will take place, in its final you can literally hear how the heartbeat of dying animal is stopped, and see how the balloons fly away in the sky, that symbolizing the souls of friends tortured and killed by people. The action will participate famous culture and art persons of Ukraine together with their four-paw pets. Animal protectors make an appeal to all concerned to join the action.
Organizers of the rally:
· Громадянська ініціатива «Форум допомоги безпритульним тваринам Ua-vet» / Civic Initiative Forum of help to homeless animals Ua-vet »
· Асоціація зоозахисних організацій України (АЗОУ) / Association of Animal Protect Organizations of Ukraine (AZOU)
· ГО «Інститут сучасних проблем України» (ГО ІСПУ) Public organization "Institute for Contemporary issues in Ukraine"
· Громадянська кампанія «Форум порятунку Києва» Civil campaign "Forum of Kyiv Saving"
· Київське товариство захисту тварин (КТЗЖ) / Kyiv Society for the Protection of Animals
· Київське міське товариство захисту тварин (КМТЗЖ) / Kiev City Society for the Protection of Animals
· Благодійне товариство «Дайте жити тварині» / Charitable Society "Give live to animals"
· ГО «Захист тварин» Святошинського р-ну м. Києва / Public organization "Protection of animals" in Sviatoshynsky district of Kyiv.
· Товариство захисту тварин «Захист» Оболонського р-ну м. Києва / Society for the Protection of Animals Protection in "Obolonsky district of Kyiv
· Товариство захисту тварин в Шевченківському районі м. Києва / Society for the Protection of Animals in the Shevchenko district of Kyiv
· Київське міське товариство гуманного ставлення до тварин «Ноєв Ковчег» / Kiev City Society for the humane treatment of animals "Noah's Ark".
With the support of the Молодіжної ліги захисту тварин (Youth League of Animal Protection).
Thanks Tatiana Metelova <metellt@rambler.ru> for article!
Thanks Oksana http://smijana.livejournal.com for help in translation!
Stepan Khrabrov 🙂
http://www.myspace.com/545843366/blog/541051470
Filed under: CAMPAIGNS - Global Animal Welfare Issues, GENERAL NEWS - International / National / Regional |	Leave a comment »
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Luisa Fernandez on The Terrible Situation for Stray Dogs In Azerbaijan – Take Action Now ! – Click on Link Below.
Free Tibet From Chinese Persecution. An Introduction To The London Based 'Free Tibet' Campaign. | Serbian Animals Voice (SAV) | United Nations For Tibet on Free Tibet From Chinese Persecution. An Introduction To The London Based 'Free Tibet' Campaign.
Dalia Domingos on Picture of the year 2020
Stacey on Global: Photographers Capture Powerfully Moving Images of Animal Exploitation Around the World.
Cyprus: poachers have an easy job now !! — Serbian Animals Voice (SAV) – spiritandanimal.wordpress.com on Cyprus: poachers have an easy job now !!
The barbaric spectacle with Thailand's boxing orangutans
The wrong side of the entertainment
USA: Michigan Teenager Wins $50,000 In Writing Competition and Donates It All to Local Cat Shelter Where She Volunteers !! – Wow !
Russia: Petition – Justice for Baby Lion Cub Blinded for Life By Illegal Wildlife Trade. Sign Now Please and Pass On.
Beauties under water
USA: Michigan Teenager Wins $50,000 In Writing Competition and Donates It All to Local Cat Shelter Where She Volunteers !! - Wow !
Serbia: Petition - End Zoophilic Tourism In Serbia!
Japan: Japanese Animal Abusing Wrestlers About to Start USA Tour - Please Take Action
England: Plastic Planet ?
USA: First case of coronavirus detected in wild animal.
The monkeys brothels of Indonesia.
New Viva! Research: 9 in 10 Britons want intensive farming methods BANNED.
UK Prime Minister Boris Johnson blames Chinese for coronavirus pandemic telling world leaders it was triggered by 'demented' people who 'grind up the scales of a pangolin' in a bid to become more 'potent'.
Japan: Campaigners condemn killing of minke whale trapped in nets in Japan.
The suffering of the mast rabbit
Bed problems


Most eye surgery operations will usually require the patient to undergo a local anaesthetic and for this reason you will in most cases be awake throughout the operation. The corrective surgery will usually be done one eye at a time and the pain is really minimal or non-existent. Plus, the surgeons will usually administer a sedative to keep you relaxed against the strain of the props used to keep your eyelid open, etc.
The pain relief during eye surgery is usually in the form of eye drops and tablets given to you before the operation begins. You might experience a bit more discomfort and pain after the operation itself in fact, after the local anaesthetic has worn off. But your surgeons will normally give you pain relief medications for use at home after the surgery. During the operation itself you may experience discomfiting pressure on the eye as the surgeon works, but it will be bearable and nothing to be too concerned about.
Preparation is the best defence as they always tell us, and it will pay dividends if you find out beforehand what to expect from an eye surgery procedure. Ask friends and anyone you know who has undergone the operation. They will usually be forthcoming and the information you gain from these sources will go a long way towards putting your mind at ease. The other thing to keep in mind is to consider the alternative to not having the operation. Given what you stand to gain by having the operation any amount of pain that might come with it is surely bearable, right? But take it from this source that you won't have to suffer much pain for eye surgery. If you have been to a dentist, this is small potatoes compared to that!
In case your surgeon uses lasers to do the incisions and the sculpting one thing to keep in mind is that the way a laser cuts is completely different from what happens when the surgeon uses knives (or scalpels). A laser incision causes very little pain simply because it vaporises, and therefore kills, any cells in the area, and this includes the pain sensor nerves. The effect is similar to being shot and only realising this after several hours, when the body has had an opportunity to re-direct the sensory information.
Eye surgery these days has benefited a lot from advances in technology that the procedures are not anywhere near as painful as they used to be. Especially where the surgery uses lasers, the entire process is not only painless, but also high-precision and accurate. There is very little room for error and you should not worry too much about outcomes – or pain relief during eye surgery or after – because if you get any at all it should be bearable, given what you stand to gain from having the operation.


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Home Scandal and Gossip Foul play? Chaffee County mom of two missing after going for bike...
Foul play? Chaffee County mom of two missing after going for bike ride on Mother's day
Suzanne Morphew missing. Photo via Facebook.
Suzanne Morphew missing: Maysville, Chaffee County, Colorado married mother of two fails to return after going for a bike ride on Mother's Day. Foul play?
A Colorado woman has gone missing after she went for a bike ride on Mother's Day, and hasn't been seen since, authorities said.
Suzanne Morphew, a 49-year-old married mother of two, was reported missing Sunday by a neighbor after she did not return from a bike ride near County Road 225 and West Highway 50 in Chaffee County, sheriff officials announced Monday.
A search for Morphew, of Maysville, involving more than 100 law enforcement officials — including some with tracking dogs — that 'began immediately,' only for no sign of the woman to be found.
The Chaffee County womans remains missing as of early Wednesday according to Chaffee County Sheriff John Spezze.
Spezze declined to comment on whether Morphew's husband, Barry, is cooperating with authorities in the search for the woman or whether investigators believe there's foul play involved.
The couple has been married close to three decades and lived in Indiana before relocating to Colorado.
Spezze has since revealed the case is an open criminal investigation the nypost reports.
Anyone with information about Morphew's whereabouts is asked to contact sheriff officials at (719) 207-3199.
Suzanne Morphew pictured with husband, Barry.
CHAFFEE COUNTY SHERIFF'S OFFICESalida, ColoradoPRESS RELEASEMay 11, 2020On May 10, 2020 at 1746 hours the…
Posted by Chaffee County Sheriff's Office on Monday, May 11, 2020
Pictured Suzanne Morphew with husband Barry and the couple's 2 daughters.