transformers pytorch bart text2text-generation summarization en dataset:yuningm/citesum arxiv:2205.06207 license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- yuningm/citesum
language: en
license: cc-by-nc-4.0
tags:
- summarization
widget:
- text: Abstract-This paper presents a control strategy that allows a group of mobile
    robots to position themselves to optimize the measurement of sensory information
    in the environment. The robots use sensed information to estimate a function indicating
    the relative importance of different areas in the environment. Their estimate
    is then used to drive the network to a desirable placement configuration using
    a computationally simple decentralized control law. We formulate the problem,
    provide a practical control solution, and present the results of numerical simulations.
    We then discuss experiments carried out on a swarm of mobile robots.
  example_title: Networked Robots
- text: Abstract. In this paper, a Bayesian method for face recognition is proposed
    based on Markov Random Fields (MRF) modeling. Constraints on image features as
    well as contextual relationships between them are explored and encoded into a
    cost function derived based on a statistical model of MRF. Gabor wavelet coefficients
    are used as the base features, and relationships between Gabor features at different
    pixel locations are used to provide higher order contextual constraints. The posterior
    probability of matching configuration is derived based on MRF modeling. Local
    search and discriminate analysis are used to evaluate local matches, and a contextual
    constraint is applied to evaluate mutual matches between local matches. The proposed
    MRF method provides a new perspective for modeling the face recognition problem.
    Experiments demonstrate promising results.
  example_title: Bayesian Face Recognition
- text: Abstract One of the most relevant applications of digital image forensics
    is to accurately identify the device used for taking a given set of images, a
    problem called source identification. This paper studies recent developments in
    the field and proposes the mixture of two techniques (Sensor Imperfections and
    Wavelet Transforms) to get better source identification of images generated with
    mobile devices. Our results show that Sensor Imperfections and Wavelet Transforms
    can jointly serve as good forensic features to help trace the source camera of
    images produced by mobile phones. Furthermore, the model proposed here can also
    determine with high precision both the brand and model of the device.
  example_title: Source identification for mobile devices
---
# Bart-Large CiteSum (Titles)

This is facebook/bart-large fine-tuned on CiteSum. The "src" column is the input and the "title" column is the target summarization.

## Authors
### Yuning Mao, Ming Zhong, Jiawei Han
#### University of Illinois Urbana-Champaign  
{yuningm2, mingz5, hanj}@illinois.edu

## Results

```
{
    "epoch": 6.78,
    "eval_gen_len": 17.1775,
    "eval_loss": 1.9626615047454834,
    "eval_rouge1": 51.4834,
    "eval_rouge2": 29.9178,
    "eval_rougeL": 45.4882,
    "eval_rougeLsum": 45.517,
    "eval_runtime": 351.9638,
    "eval_samples": 4681,
    "eval_samples_per_second": 13.3,
    "eval_steps_per_second": 0.21,
    "predict_gen_len": 17.1032,
    "predict_loss": 1.9391602277755737,
    "predict_rouge1": 52.0304,
    "predict_rouge2": 30.1511,
    "predict_rougeL": 45.9902,
    "predict_rougeLsum": 46.0068,
    "predict_runtime": 363.9691,
    "predict_samples": 4882,
    "predict_samples_per_second": 13.413,
    "predict_steps_per_second": 0.212,
    "train_loss": 1.0821667497907366,
    "train_runtime": 24401.3762,
    "train_samples": 82653,
    "train_samples_per_second": 65.57,
    "train_steps_per_second": 8.196
}
```

## Dataset Description
CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation.  
CiteSum contains TLDR summaries for scientific papers from their citation texts without human annotation, making it around 30 times larger than the previous human-curated dataset SciTLDR.
## Homepage
https://github.com/morningmoni/CiteSum
## Paper
https://arxiv.org/abs/2205.06207

## Dataset on Hub
https://huggingface.co/datasets/nbroad/citesum

## How to use model

```python
from transformers import pipeline
summarizer = pipeline("summarization", model="yuningm/bart-large-citesum-title")

article = ''' We describe a convolutional neural network that learns\
 feature representations for short textual posts using hashtags as a\
  supervised signal. The proposed approach is trained on up to 5.5 \
  billion words predicting 100,000 possible hashtags. As well as strong\
   performance on the hashtag prediction task itself, we show that its \
   learned representation of text (ignoring the hashtag labels) is useful\
    for other tasks as well. To that end, we present results on a document\
     recommendation task, where it also outperforms a number of baselines.
'''
summarizer(article)
# [{'summary_text': 'Learning Text Representations from Hashtags using Convolutional Neural Networks'}]
```

