Extractive Summarization of a Document Using Lexical Chains

Mallick, Chirantana; Dutta, Madhurima; Das, Ajit Kumar; Sarkar, Apurba; Das, Asit Kumar

doi:10.1007/978-981-13-0514-6_78

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…Generally, lexical chains provide a better indication of discourse topic than does word frequency simply because different words may refer to the same topic. Even without sense disambiguation, these approaches can derive concepts [20].…”

Section: Lexical Chain Sentence (Lcs)mentioning

confidence: 99%

New algorithm for clustering unlabeled big data

Mohammed

Al-Hameed

2021

IJEECS

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The clustering analysis techniques play an important role in the area of data mining. Although from existence several clustering techniques. However, it still to their tries to improve the clustering process efficiently or propose new techniques seeks to allocate objects into clusters so that two objects in the same cluster are more similar than two objects in different clusters and careful not to duplicate the same objects in different groups with the ability to cover all data as much as possible. This paper presents two directions. The first is to propose a new algorithm that coined a name (MB Algorithm) to collect unlabeled data and put them into appropriate groups. The second is the creation of a lexical sequence sentence (LCS) based on similar semantic sentences which are different from the traditional lexical word chain (LCW) based on words. The results showed that the performance of the MB algorithm has generally outperformed the two algorithms the hierarchical clustering algorithm and the K-mean algorithm.

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Section: Lexical Chain Sentence (Lcs)mentioning

confidence: 99%

New algorithm for clustering unlabeled big data

Mohammed

Al-Hameed

2021

IJEECS

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“…Finally, the graph is sparsed by partitioning into different clusters for generating summary. Another extractive summarization described in [27] uses lexical chain to set up cohesion links among the words by determining their semantic relationships. The survey of Text summarization system [1] proposes linguistic methods for textual-based summarization for generating meaningful and informed summary of input text documents.…”

Section: Background Studymentioning

confidence: 99%

An Unsupervised COVID-19 Report Summarizer for Developing Smart Healthcare System

Das

Pal²,

Mallick

et al. 2021

Advances in Intelligent Systems and Computing

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Due to the coronavirus pandemic, COVID-19 reports are being generated frequently and volume is increasing exponentially. These reports provide innovative information to healthcare and medical science. Analysis of this information helps us to extract meaningful knowledge for improving our healthcare systems. An automated report summarizer is a model whose task is to rewrite a report into a very compact form without losing any of its important content. In the paper, the COVID-19 reports collected from the new release open-source, CORD-19 dataset, are applied in our proposed automated text summarizer. Initially, we have preprocessed the reports by Natural Language Processing (NLP) and subsequently, the Unified Medical Language System (UMLS) tool has been used to bring out only the medical terms along with the concepts corresponding to that medical terms. So, a set of concepts represent each sentence in a report and with the help of a word-to-vector-based neural network model that extracted concepts are converted into fixed dimension Word2vec vectors. Next, the fixed dimension real-valued vectors replace the set of concepts corresponding to each sentence using the summation scheme. Thus, the report is described by a real-valued dataset. Finally, the unsupervised learning (i.e. clustering algorithm) has been applied for the dataset to partition every sentence into different clusters and a representative from each cluster is identified based on Pearson correlation and clustering coefficient. The representative sentences collected using clusters are arranged according to their appearances in the report, which is the desired summary of the report. In the paper, different clustering algorithms are applied, so different A. Das (B)

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“…Lexical chain extraction (LCE) is an important research topic in the natural language processing (NLP) community, aiming to group cohesion words in a document into one cluster [20]. Lexical chain implies the underlying structure about the texts and provides practical cues for facilitating downstream NLP tasks, e.g., text summarization [1,18], keyword extraction [2,6], headline generation [24,23], etc.…”

Section: Introductionmentioning

confidence: 99%

Nominal Compound Chain Extraction: A New Task for Semantic-enriched Lexical Chain

Fei

Ren

et al. 2020

Preprint

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Lexical chain consists of cohesion words in a document, which implies underlying structure of a text, and thus facilitates downstream NLP tasks. Nevertheless, existing work focuses on detecting the simple surface lexicons with shallow syntax associations, ignoring the semantic-aware lexical compounds as well as the latent semantic frames, (e.g., topic), which can be much more crucial for real-world NLP applications. In this paper, we introduce a novel task, Nominal Compound Chain Extraction (NCCE), extracting and clustering all the nominal compounds that share identical semantic topics. In addition, we model the task as a two-stage prediction (i.e., compound extraction and chain detection), which is handled via a proposed joint framework. The model employs the BERT encoder to yield contextualized document representation. Also HowNet is exploited as external resource for offering rich sememe information. The experiments are based on our manually annotated corpus, and the results prove the necessity of the NCCE task as well as the effectiveness of our joint approach.

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Extractive Summarization of a Document Using Lexical Chains

Cited by 12 publications

References 18 publications

New algorithm for clustering unlabeled big data

New algorithm for clustering unlabeled big data

An Unsupervised COVID-19 Report Summarizer for Developing Smart Healthcare System

Nominal Compound Chain Extraction: A New Task for Semantic-enriched Lexical Chain

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