Text document summarization using word embedding

Mohd, Mudasir; Jan, Rafiya; Shah, Muzaffar Bashir

doi:10.1016/j.eswa.2019.112958

Cited by 96 publications

(52 citation statements)

References 16 publications

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“…In this area, a major breakthrough is achieved through deep learning techniques. Several researchers have worked on Convolution neural network (CNN) [16], Recurrent neural network (RNN) [17], Reinforcement learning and Generative Adversarial networks (GAN) [18] with high accuracy as compared to other approaches used in literature. The major shortcoming of applying deep learning methods is the un-availability of training data as it is a supervised approach and standard golden summaries are not available in every domain.…”

Section: To Oversee Diverse Number Of Bug Reports Several Automationmentioning

confidence: 99%

Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Jindal

Kaur

2020

IEEE Access

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Text Summarization is a process which efficiently retrieves the relevant information from documents. The objective of the proposed, unsupervised approach is to summarize bug reports (software artefacts) with complete content and diversified information. The proposed approach utilizes Rapid Automatic Keyword Extraction and term frequency-inverse document frequency method to extract meaningful keywords and key-phrases with a relevant score. For sentence extraction, fuzzy C-means clustering is used to extracts sentences having high degree of membership from each cluster above a set threshold value. A ruleengine is used for sentence selection. The rules are generated with the domain knowledge and based on the extracted information by the keywords and sentences selected by the clustering method. Cohesive and coherent summary is generated by the proposed method on apache bug reports. For redundancy removal and to re-rank generated summary, hierarchical clustering is presented to enrich the extracted summary. The proposed approach is evaluated on newly constructed Apache project Bug Report Corpus (APBRC) and existing Bug Report Corpus (BRC). The results are compared on the basis of performance metrics such as precision, recall, pyramid precision and F-score. The experimental results depict that our proposed approach attains significant improvement over other baseline approaches such as BRC and LRCA. It also attains significant improvement over existing state-of-art unsupervised approaches such as Hurried, centroid and others. It extracts significant keyword phrases and sentences from each cluster to achieve full coverage and coherent summary. The results evaluated on APBRC corpus attains an average value of 78.22%, 82.18%, 80.10% and 81.66% for precision, recall, f-score and pyramid precision respectively. INDEX TERMS Text summarization, rapid automatic keyword extraction, fuzzy c-means, hierarchical clustering, bug reports, rule engine.

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Section: To Oversee Diverse Number Of Bug Reports Several Automationmentioning

confidence: 99%

Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Jindal

Kaur

2020

IEEE Access

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“…X X ! Mainly focused on text cleansing with no corpus processing reduction Extractive summarization based semantic framework [10] ! X !…”

Section: Concept-based Abstractive and Extractive Text Mining Implementmentioning

confidence: 99%

“…After the metadata processing, extractive summarization reduces the corpus by removing less necessary text [3], [10]. The potential sentences are either selected through the sentence score algorithm or word embedding principles, i.e., Word2Vec model [11], [18], [29].…”

Section: Introductionmentioning

confidence: 99%

Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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In natural language processing, text summarization is an important application used to extract desired information by reducing large text. Existing studies use keyword-based algorithms for grouping text, which do not give the documents' actual theme. Our proposed dynamic corpus creation mechanism combines metadata with summarized extracted text. The proposed approach analyzes the mesh of multiple unstructured documents and generates a linked set of multiple weighted nodes by applying multistage Clustering. We have generated adjacency graphs to link the clusters of various collections of documents. This approach comprises of ten steps: pre-processing, making multiple corpuses, first stage clustering, creating sub-corpuses, interlinking sub-corpuses, creating page rank keyword dictionary of each sub-corpus, second stage clustering, path creation among clusters of sub-corpuses, text processing by forward and backward propagation for results generation. The outcome of this technique consists of interlinked subcorpuses through clusters. We have applied our approach to a News dataset, and this interlinked corpus processing follows step by step clustering to search the most relevant parts of the corpus with less cost, time, and improve content detection. We have applied six different metadata processing combinations over multiple text queries to compare results during our experimentation. The comparison results of text satisfaction show that Page-Rank keywords give 38% related text, single-stage Clustering gives 46%, twostage Clustering gives 54%, and the proposed technique gives 67% associated text. Furthermore, this approach covers/searches the relevant data with a range of most to less relevant content. It provides the systematic query-relevant corpus processing mechanism, which automatically selects the most relevant subcorpus through dynamic path selection. We used the SHAP model to evaluate the proposed technique, and our evaluation results proved that the proposed mechanism improved text processing. Moreover, combining text summarization features, shown satisfactory results compared to the summaries generated by general models of abstractive & extractive summarization.

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“…Others have intrinsically evaluated word embeddings by clustering biomedical terms from the Unified Medical Language System and Ranker [ 21 ], and assessing the cluster quality using metrics like the Davies-Bouldin index and the Dunn index. Word embeddings have advanced the state of the art for many intrinsic natural language processing subtasks (ie, reading comprehension [ 22 ], natural language inference [ 23 ], text summarization [ 24 ], vocabulary development [ 8 ], and document classification [ 25 ]). An extrinsic or summative evaluation of clinical word embeddings can involve evaluating the performance of machine learning models by using word embeddings to complete a biomedical research task or clinical operation such as patient phenotyping [ 26 , 27 ], patient fall prediction [ 25 ], and patient hospital readmission prediction [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation

Parikh¹,

Davoudi²,

Yu³

et al. 2021

JMIR Med Inform

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Background Scientists are developing new computational methods and prediction models to better clinically understand COVID-19 prevalence, treatment efficacy, and patient outcomes. These efforts could be improved by leveraging documented COVID-19–related symptoms, findings, and disorders from clinical text sources in an electronic health record. Word embeddings can identify terms related to these clinical concepts from both the biomedical and nonbiomedical domains, and are being shared with the open-source community at large. However, it’s unclear how useful openly available word embeddings are for developing lexicons for COVID-19–related concepts. Objective Given an initial lexicon of COVID-19–related terms, this study aims to characterize the returned terms by similarity across various open-source word embeddings and determine common semantic and syntactic patterns between the COVID-19 queried terms and returned terms specific to the word embedding source. Methods We compared seven openly available word embedding sources. Using a series of COVID-19–related terms for associated symptoms, findings, and disorders, we conducted an interannotator agreement study to determine how accurately the most similar returned terms could be classified according to semantic types by three annotators. We conducted a qualitative study of COVID-19 queried terms and their returned terms to detect informative patterns for constructing lexicons. We demonstrated the utility of applying such learned synonyms to discharge summaries by reporting the proportion of patients identified by concept among three patient cohorts: pneumonia (n=6410), acute respiratory distress syndrome (n=8647), and COVID-19 (n=2397). Results We observed high pairwise interannotator agreement (Cohen kappa) for symptoms (0.86-0.99), findings (0.93-0.99), and disorders (0.93-0.99). Word embedding sources generated based on characters tend to return more synonyms (mean count of 7.2 synonyms) compared to token-based embedding sources (mean counts range from 2.0 to 3.4). Word embedding sources queried using a qualifier term (eg, dry cough or muscle pain) more often returned qualifiers of the similar semantic type (eg, “dry” returns consistency qualifiers like “wet” and “runny”) compared to a single term (eg, cough or pain) queries. A higher proportion of patients had documented fever (0.61-0.84), cough (0.41-0.55), shortness of breath (0.40-0.59), and hypoxia (0.51-0.56) retrieved than other clinical features. Terms for dry cough returned a higher proportion of patients with COVID-19 (0.07) than the pneumonia (0.05) and acute respiratory distress syndrome (0.03) populations. Conclusions Word embeddings are valuable technology for learning related terms, including synonyms. When leveraging openly available word embedding sources, choices made for the construction of the word embeddings can significantly influence the words learned.

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Text document summarization using word embedding

Cited by 96 publications

References 16 publications

Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Unstructured Text Documents Summarization With Multi-Stage Clustering

Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation

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