Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain

Abstract

For the better utilization of the enormous amount of data available to us on the Internet and in different archives, summarization is a valuable method. Manual summarization by experts is an almost impossible and time-consuming activity. People could not access, read, or use such a big pile of information for their needs. Therefore, summary generation is essential and beneficial in the current scenario. This paper presents an efficient qualitative analysis of the different algorithms used for text summarization. We implemented five different algorithms, namely, term frequency-inverse document frequency (TF-IDF), LexRank, TextRank, BertSum, and PEGASUS, for a summary generation. These algorithms are chosen based on various factors. After reviewing the state-of-the-art literature, it generates good summaries results. The performance of these algorithms is compared on two different datasets, i.e., Reddit-TIFU and MultiNews, and their results are measured using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure to perform analysis to decide the best algorithm among these and generate the summary. After performing a qualitative analysis of the above algorithms, we observe that for both the datasets, i.e., Reddit-TIFU and MultiNews, PEGASUS had the best average F-score for abstractive text summarization and TextRank algorithms for extractive text summarization, with a better average F-score.

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

“…A text extractor can be built using the AI technique for the automated extraction of key terms from a document [28][29][30][31][32][33][34]. An ontology-based dynamic information extraction framework identifies a wide range of document resources published in the scientific community and extracts the whole structural information [35][36][37][38][39][40][41]. e accuracy and scope of information extraction can be improved using an entity-relationship-based framework [42][43][44][45][46][47].…”

“…e internal communication between employees generates a larger amount of documents [31][32][33][34][35]. Organizations demand an arti cial intelligence (AI) based system to generate knowledge from the documents [36][37][38][39][40][41].…”

Single Document Text Summarization Based on the Modified Cat Swarm Optimization (Mcso) Algorithm

“…Abstractive summaries requirehigh-level etymological procedures to reword new sentences which are not in unique reports thus they are a lot more difficult. Extractive summarization methods often use features like sentence importance scores, sentence length, keyword frequency, and other heuristics to establish which condemnationought to be included in the summary and this gives the short content of the document [12][13][14][15]. Different types of methods have been used for carrying out extractive text summarization namely supervised, unsupervised, and meta-heuristic.…”

Retracted: Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”