TEXT SUMMARIZATION AS AN EMPIRICAL LEGAL RESEARCH TOOL BASED ON MACHINE LEARNING ALGORITHMS

This paper aims to explore text summarization techniques as a tool for empirical legal research, creating a summary of the decisions given the phrases predictive power with regard to the decision outcome. A dataset of habeas corpus decisions prompted by innumerable courts in Brazil is used that explicitly cite the COVID pandemic as a reason for requesting the release of the patients. A predictive model is created and through this analysis we propose to find the arguments most correlated with the outcome. © 2022 Universidad Diego Portales. All rights reserved.

A novel centroid based sentence classification approach for extractive summarization of COVID-19 news reports.

A COVID-19 news covers subtopics like infections, deaths, the economy, jobs, and more. The proposed method generates a news summary based on the subtopics of a reader's interest. It extracts a centroid having the lexical pattern of the sentences on those subtopics by the frequently used words in them. The centroid is then used as a query in the vector space model (VSM) for sentence classification and extraction, producing a query focused summarization (QFS) of the documents. Three approaches, TF-IDF, word vector averaging, and auto-encoder are experimented to generate sentence embedding that are used in VSM. These embeddings are ranked depending on their similarities with the query embedding. A Novel approach has been introduced to find the value for the similarity parameter using a supervised technique to classify the sentences. Finally, the performance of the method has been assessed in two different ways. All the sentences of the dataset are considered together in the first assessment and in the second, each document wise group of sentences is considered separately using fivefold cross-validation. The proposed method has achieved a minimum of 0.60 to a maximum of 0.63 mean F1 scores with the three sentence encoding approaches on the test dataset.

Automatic Text Summarization of COVID-19 Scientific Research Topics Using Pre-trained Models from Hugging Face

Automated text summarizing helps the scientific and medical sectors by identifying and extracting relevant information from articles. Automatic text summarization is a way of compressing text documents so that users may find important and useful information in the original text in reduced time. We will first review some new works in the field of summarization that uses deep learning approaches, and then we will explain the application to COVID-19 related research papers. The ease with which a reader can grasp written text is referred to as the readability test. The substance of text determines its readability in natural language processing. We constructed word clouds using the s' most commonly used text. By looking at those three measurements, we can determine the performance measures of ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-L-SUM. Our findings indicated that Distilbart-mnli-12-6 and GPT2-large outperform than others considered. © 2022 IEEE.

Hybridization of Fuzzy Theory and Nature-Inspired Optimization for Medical Report Summarization

It is becoming increasingly challenging to construct a smart medical system because of the large amount of accumulating information in the scientific literature of the biomedical sector. The current scenario reflects progress in a variety of less known regions as a means of extraction for the understanding of prevention and treatment for significant medical diseases such as COVID-19. Recently, many good scientific research publications in the biomedical arena was released using the MEDLINE/PubMed dataset. In the fields of biomedical research and healthcare, assessing these enormous data sets and extracting valuable information is a critical but difficult endeavour. Here, we attempt to retrieve relevant data from openly accessible text materials, like medical reports, journals, articles, papers, and some other research works, in the medical area. hese types of text data undergo first preprocessing in this chapter using sentence tokenization, then stopword removal, stemming operations, and ultimately vectorization using the BioBERT model. Consequently, a structured data is generated to process each report in feature extraction process and then clustered the similar sentences by Fuzzy C-means clustering. Then, using multiple similarity clustering measures and a bi-objective strength measure, defuzzify the clusters and construct the base summaries. To construct the report summary, en ensemble summarising approach has been employed by using Pareto evolutionary algorithm. The method contains two optimization methods(or functions): one dependent on the produced summary size, which is constant, and the other dependent on the IG (i.e., information gain) of the considering base summaries, which is variable. When the process of evolution converges, the strongest chromosomal solution of the ultimate population offers a desired summary report. This approach is used to generate an efficient summary from biomedical reports publically available in the MEDLINE/PubMed dataset, and finally, its performance comparing with a few similar cutting-edge techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstractive Text Summarization for Covid-19 News with Data Augmentation

Since the spread of Corona Virus disease or Covid-19 at the end of 2019, there has been an extensive amount of news about Covid-19 and it takes a long time for humans to read the news, process it and retrieve important information from it. Therefore, automatic text summarization is necessary in this matter as it can help us process information faster and use it to make better decisions. Currently, there are two main approaches to automatic text summarization: extractive and ive. Extractive text summarization is conducted by identifying important parts of the text and extract a subset of sentences from the original text. ive text summarization is closer to human's method as it is the reproduction or rephrasing based on interpretation and understanding of the text using natural language processing techniques. In this paper, we present text summarization of Covid-19 news using ive method to be close to human's method of summary. We also apply data augmentation in the pre-processing part to be an example case of working with data that are not perfect or diverse enough. © 2022 IEEE.

Graph-Based Text Summarization and Its Application on COVID-19 Twitter Data

Large volumes of structured and semi-structured data are being generated every day. Processing this large amount of data and extracting important information is a challenging task. The goal of an automatic text summarization is to preserve the key information and the overall meaning of the article to be summarized. In this paper, a graph-based approach is followed to generate an extractive summary, where sentences of the article are considered as vertices, and weighted edges are introduced based on the cosine similarities among the vertices. A possible subset of maximal independent sets of vertices of the graph is identified with the assumption that adjacent vertices provide sentences with similar information. The degree centrality and clustering coefficient of the vertices are used to compute the score of each of the maximal independent sets. The set with the highest score provides the final summary of the article. The proposed method is evaluated using the benchmark BBC News data to demonstrate its effectiveness and is applied to the COVID-19 Twitter data to express its applicability in topic modeling. Both the application and comparative study with other methods illustrate the efficacy of the proposed methodology.

Machine Propositional Model Research Based on Natural Language Processing

Machine propositions are the research frontier of natural language processing technology, and the core of the technology path is the result of the development of reading comprehension and question answering systems. Machine propositions can help save a lot of manpower and time, especially in the case of the continuous development of the Covid-19, and help the teaching acceptance and learning evaluation of online learning. At present, the simultaneous pursuit of autonomous propositions and precision requirements in the academic field of machine propositions is the research focus and difficulty. © 2022 IEEE.

Extractive Text Summarization for COVID-19 Medical Records

The method of reducing information from an original text document while maintaining the vital information is known as text summarizing. The amount of text data available has increased dramatically in recent years from a variety of sources. A large volume of text is an excellent source of information and knowledge of the source is essential for efficiently summarizing information that must be useful. Summarization facilitates the acquisition of vital and required information in a short period of time. Text summarization is required in a variety of domains, including news article summaries, email summaries and information summaries in the medical profession to track a patient's medical history for future treatment and so on. In summarization, there are two methods: extractive summarization and ive summarization. In this work, extractive summarization is used on the COVID-19 dataset. Different models and their results have been discussed. © 2021 IEEE.

Information Extraction from CORD-19 Using Hierarchical Clustering and Word Bank

The Coronavirus pandemic has called for extensive research in the medical discipline. Since such disease outbreaks are about life and death of the patients, the doctors' and biomedical scientists' time is crucial. Research documents are usually comprehensive, often consuming the readers' time. A solution to it would be to extract information from the research text resembling the most relevant parts of the original text so that their valuable time is saved. The problem of text summarization is to create a shortened piece of text that represents the most relevant information from a relatively larger piece of text. This paper aims to ease the burden of the doctors so won't have to read the extensive research documents by constructing a summary of the most relevant parts of a medical research paper. A text summarization algorithm always works by quantifying the sentences by some means and analyzing scores. We use the TF-IDF quantification which is a popular way to quantify sentences. We select sentences with a high score and exclude those with a lower score, compared to a threshold. In a medical research paper, several sentences might have a low score, but they might be important if they contain biomedical entities. We use a dataset which has been constructed upon biomedical and COVID-19 terminology to construct a much better summary than some existing tools. As new methods keep coming up, this simple, yet robust approach gives us an accuracy of over 78% for most CORD-19 research papers. © 2021 IEEE.

A Light-Weight Text Summarization System for Fast Access to Medical Evidence.

As the volume of published medical research continues to grow rapidly, staying up-to-date with the best-available research evidence regarding specific topics is becoming an increasingly challenging problem for medical experts and researchers. The current COVID19 pandemic is a good example of a topic on which research evidence is rapidly evolving. Automatic query-focused text summarization approaches may help researchers to swiftly review research evidence by presenting salient and query-relevant information from newly-published articles in a condensed manner. Typical medical text summarization approaches require domain knowledge, and the performances of such systems rely on resource-heavy medical domain-specific knowledge sources and pre-processing methods (e.g., text classification) for deriving semantic information. Consequently, these systems are often difficult to speedily customize, extend, or deploy in low-resource settings, and they are often operationally slow. In this paper, we propose a fast and simple extractive summarization approach that can be easily deployed and run, and may thus aid medical experts and researchers obtain fast access to the latest research evidence. At runtime, our system utilizes similarity measurements derived from pre-trained medical domain-specific word embeddings in addition to simple features, rather than computationally-expensive pre-processing and resource-heavy knowledge bases. Automatic evaluation using ROUGE-a summary evaluation tool-on a public dataset for evidence-based medicine shows that our system's performance, despite the simple implementation, is statistically comparable with the state-of-the-art. Extrinsic manual evaluation based on recently-released COVID19 articles demonstrates that the summarizer performance is close to human agreement, which is generally low, for extractive summarization.