readme: ---
  language: en
  tags:
  - Summarization
  license: apache-2.0
  datasets:
  - scientific_papers
  - big_patent
  - cnn_corpus
  - cnn_dailymail
  - xsum
  - MCTI_data
  thumbnail: https://github.com/Marcosdib/S2Query/Classification_Architecture_model.png
  ---
  
  ![MCTIimg](https://antigo.mctic.gov.br/mctic/export/sites/institucional/institucional/entidadesVinculadas/conselhos/pag-old/RODAPE_MCTI.png)
  
  
  # MCTI Text Automatic Text Summarization Task (uncased) DRAFT
  
  Disclaimer: The Brazilian Ministry of Science, Technology, and Innovation (MCTI) has partially supported this project.
  
  ## According to the abstract of the literature review,
  
  - We provide a literature review about Automatic Text Summarization systems. We consider a citation-based approach. We start with some popular and well-known 
    papers that we have in hand about each topic we want to cover and we have tracked the "backward citations" (papers that are cited by the set of papers we 
    knew beforehand) and the "forward citations" (newer papers that cite the set of papers we knew beforehand). In order to organize the different methods, we 
    present the diverse approaches to ATS guided by the mechanisms they use to generate a summary. Besides presenting the methods, we also present an extensive 
    review of the datasets available for summarization tasks and the methods used to evaluate the quality of the summaries.  Finally, we present an empirical 
    exploration of these methods using the CNN Corpus dataset that provides golden summaries for extractive and abstractive methods.
  
  This model was an end result of the above-mentioned literature review paper, from which the best solution was drawn to be applied to the problem of 
  summarizing texts extracted from the Research Financing Products Portfolio (FPP) of the Brazilian Ministry of Science, Technology, and Innovation (MCTI). 
  It was first released in [this repository](https://huggingface.co/unb-lamfo-nlp-mcti), along with the other models used to address the given problem.
  
  ## Model description
  
  This Automatic Text Summarization (ATS) Model was developed in the Python language to be applied to the Research Financing Products 
  Portfolio (FPP) of the Brazilian Ministry of Science, Technology, and Innovation. It was produced in parallel with the writing of a 
  Systematic Literature Review paper, in which there is a discussion concerning many summarization methods, datasets, and evaluators
  as well as a brief overview of the nature of the task itself and the state-of-the-art of its implementation.
  
  The input of the model can be either a single text, a data frame or a CSV file containing multiple texts (in the English language) and its output
  is the summarized texts and their evaluation metrics. As an optional (although recommended) input, the model accepts gold-standard summaries 
  for the texts, i.e., human-written (or extracted) summaries of the texts which are considered to be good representations of their contents. 
  Evaluators like ROUGE, which in its many variations is the most used to perform the task, require gold-standard summaries as inputs. There are, 
  however, Evaluation Methods that do not depend on the existence of a golden summary (e.g. the cosine similarity method, the Kullback Leibler
  Divergence method) and this is why an evaluation can be made even when only the text is taken as input to the model.
  
  The text output is produced by a chosen method of ATS which can be extractive (built with the most relevant sentences of the source document) 
  or abstractive (written from scratch in an abstractive manner). The latter is achieved by means of transformers, and the ones present in the
  model are the already existing and vastly applied BART-Large CNN, Pegasus-XSUM and mT5 Multilingual XLSUM. The extractive methods are taken from
  the Sumy Python Library and include SumyRandom, SumyLuhn, SumyLsa, SumyLexRank, SumyTextRank, SumySumBasic, SumyKL and SumyReduction. Each of the
  methods used for text summarization will be described individually in the following sections.
  
  ## Methods
  
  Since there are many methods to choose from in order to perform the ATS task using this model, the following table presents useful information
  regarding each of them, such as what kind of ATS the method produces (extractive or abstractive), where to find the documentation necessary for 
  its implementation and the article from which it originated.
  
  | Method                 | Kind of ATS | Documentation   | Source Article |
  |:----------------------:|:-----------:|:---------------:|:--------------:|
  | SumyRandom             | Extractive  |   [Sumy GitHub](https://github.com/miso-belica/sumy/) | None (picks out random sentences from source text) |
  | SumyLuhn               | Extractive  |   Ibid.         | [(Luhn, 1958)](http://www.di.ubi.pt/~jpaulo/competence/general/%281958%29Luhn.pdf) |
  | SumyLsa                | Extractive  |   Ibid.         | [(Steinberger et al., 2004)](http://www.kiv.zcu.cz/~jstein/publikace/isim2004.pdf) |
  | SumyLexRank   