metadata

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

MCTI Text Automatic Text Summarization Task (uncased) DRAFT

Disclaimer:

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, along with the other models used to address the given problem.

Model description

This Automatic Text Summarizarion (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 Sistematic 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 dataframe or a csv file containing multiple texts (in the English language) and its output are 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 which do not deppend 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 an 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 indvidually 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	None (picks out random sentences from source text)
SumyLuhn	Extractive	Ibid.	(Luhn, 1958)
SumyLsa	Extractive	Ibid.	(Steinberger et al., 2004)
SumyLexRank	Extractive	Ibid.	(Erkan and Radev, 2004)
SumyTextRank	Extractive	Ibid.	(Mihalcea and Tarau, 2004)
SumySumBasic	Extractive	Ibid.	(Vanderwende et. al, 2007)
SumyKL	Extractive	Ibid.	(Haghighi and Vanderwende, 2009)
SumyReduction	Extractive	Ibid.	None.
BART-Large CNN	Abstractive	facebook/bart-large-cnn	(Lewis et al., 2019)
Pegasus-XSUM	Abstractive	google/pegasus-xsum	(Zhang et al., 2020)
mT5 Multilingual XLSUM	Abstractive	csebuetnlp/mT5_multilingual_XLSum	(Raffel et al., 2019)

Limitations

[PERGUNTAR ARTHUR]

How to use

Initially, some libraries will need to be imported in order for the program to work. The following lines of code, then, are necessary:

import threading
from alive_progress import alive_bar
from datasets import load_dataset
from bs4 import BeautifulSoup
import pandas as pd
import numpy as np
import shutil
import regex
import os
import re
import itertools as it
import more_itertools as mit

If any of the above mentioned libraries are not installed in the user's machine, it will be required for him to install them through the CMD with the comand:

>>> pip install [LIBRARY]

To run the code with given corpus' of data, the following lines of code need to be inserted. If one or multiple corpora, summarizers and evaluators are not to be applied, the user has to comment the unwanted option.

if __name__ == "__main__":

    corpora = [
        "mcti_data",
        "cnn_dailymail",
        "big_patent",
        "cnn_corpus_abstractive",
        "cnn_corpus_extractive",
        "xsum",
        "arxiv_pubmed",
    ]

    summarizers = [
        "SumyRandom",
        "SumyLuhn",
        "SumyLsa",
        "SumyLexRank",
        "SumyTextRank",
        "SumySumBasic",
        "SumyKL",
        "SumyReduction",
        "Transformers-facebook/bart-large-cnn",
        "Transformers-google/pegasus-xsum",
        "Transformers-csebuetnlp/mT5_multilingual_XLSum",
    ]

    metrics = [
        "rouge",
        "gensim",
        "nltk",
        "sklearn",
    ]

    ### Running methods and eval locally

    reader = Data()
    reader.show_available_databases()
    for corpus in corpora:
        data = reader.read_data(corpus, 50)
        method = Method(data, corpus)
        method.show_methods()
        for summarizer in summarizers:
            df = method.run(summarizer)
            method.examples_to_csv()
            evaluator = Evaluator(df, summarizer, corpus)
            for metric in metrics:
                evaluator.run(metric)
                evaluator.metrics_to_csv()
            evaluator.join_all_results()

Training data

In order to train the model, it's transformers were trained with five datasets, which were:

Scientific Papers (arXiv + PubMed): Cohan et al. (2018) found out that there were only datasets with short texts (with an average of 600 words) or datasets with longer texts with extractive humam summaries. In order to fill the gap and to provide a dataset with long text documents for abstractive summarization, the authors compiled two new datasets with scientific papers from arXiv and PubMed databases. Scientific papers are specially convenient given the desired kind of ATS the authors mean to achieve, and that is due to their large length and the fact that each one contains an abstractive summary made by its author – i.e., the paper’s abstract.
BIGPATENT: Sharma et al. (2019) introduced the BIGPATENT dataset that provides goods examples for the task of abstractive summarization. The data dataset is built using Google Patents Public Datasets, where for each document there is one gold-standard summary which is the patent’s original abstract. One advantage of this dataset is that it does not present difficulties inherent to news summarization datasets, where summaries have a flattened discourse structure and the summary content arises in the begining of the document.
CNN Corpus: Lins et al. (2019b) introduced the corpus in order to fill the gap that most news summarization single-document datasets have fewer than 1,000 documents. The CNN-Corpus dataset, thus, contains 3,000 Single-Documents with two gold-standard summaries each: one extractive and one abstractive. The encompassing of extractive gold-standard summaries is also an advantage of this particular dataset over others with similar goals, which usually only contain abstractive ones.
CNN/Daily Mail: Hermann et al. (2015) intended to develop a consistent method for what they called ”teaching machines how to read”, i.e., making the machine be able to comprehend a text via Natural Language Processing techniques. In order to perform that task, they collected around 400k news from the newspapers CNN and Daily Mail and evaluated what they considered to be the key aspect in understanding a text, namely the answering of somewhat complex questions about it. Even though ATS is not the main focus of the authors, they took inspiration from it to develop their model and include in their dataset the human made summaries for each news article.
XSum: Narayan et al. (2018b) introduced the single-document dataset, which focuses on a kind of summarization described by the authors as extreme summarization – an abstractive kind of ATS that is aimed at answering the question “What is the document about?”. The data was obtained from BBC articles and each one of them is accompanied by a short gold-standard summary often written by its very author.

Each of their documents was summarized through every summarization method applied in the code and evaluated in comparison with the gold-standard summaries.

Training procedure

Preprocessing

[PERGUNTAR ARTHUR]

Hey, look how easy it is to write LaTeX equations in here $A x = b$ or even $ Ax = b $

Evaluation results

Table 2: Results from Pre-trained Longformer + ML models.

| ML Model | Accuracy | F1 Score | Precision | Recall | || tentativa ||||

:--------:	:--------:	:--------:
NN	0.8269	0.8754	0.7950	0.9773
DNN	0.8462	0.8776	0.8474	0.9123
CNN	0.8462	0.8776	0.8474	0.9123
LSTM	0.8269	0.8801	0.8571	0.9091

Checkpoints

Examples
Implementation Notes
Usage Example
...

BibTeX entry and citation info

@conference{webist22,
author       ={Daniel O. Cajueiro and Maísa {Kely de Melo}. and Arthur G. Nery and Silvia A. dos Reis and Igor Tavares
              and Li Weigang and Victor R. R. Celestino.},
title        ={A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding},
booktitle    ={Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST,},
year         ={2022},
pages        ={},
publisher    ={},
organization ={},
doi          ={},
isbn         ={},
issn         ={},
}