State-of-the-art approach to extractive text summarization: a comprehensive review

Yadav, Avaneesh Kumar; Ranvijay,; Yadav, Rama Shankar; Maurya, Ashish Kumar

doi:10.1007/s11042-023-14613-9

Cited by 21 publications

(5 citation statements)

References 157 publications

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“…Choose a suitable machine learning algorithm for Named Entity Recognition [50] such as Naive Bias, CNN [26], RBM, CRF, NN, HMM, SVM, k-NN random forests, Generic Algorithms (GA) and regression etc. Machine Learning (ML) is divided into three ways like supervised, semisupervised and unsupervised learning approach [68]. It generates features from the extracted data that capture relevant information.…”

Section: (B) Machine Learning Approachmentioning

confidence: 99%

“…These tools for evaluating the criteria manually and automatically encompasses the common datasets. The most popular datasets used in NER for Indian Language and their evaluation are presented in this stateof-the-art review [25], [68] [88], [93]. Their respective details are listed in Table 7.…”

Section: Datasetmentioning

confidence: 99%

“…(b) Quality evaluation: Text quality is measured by quality evaluation, which is divided into four categories as explained below [68].…”

Section: 𝐹 − 𝑠𝑐𝑜𝑟𝑒 = 2×precision × Recallmentioning

confidence: 99%

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State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

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Named Entity Recognition (NER) is a fundamental task of natural language processing (NLP) that focuses on the identification and classification of named entities such as name of individual persons, location, organization and dates within the text. NER plays a pivotal role in various NLP applications, including information extraction, question answering, text summarization and sentiment analysis. Natural language processing's (NLPs) fundamental issue is named entity recognition (NER). While extensive research has been conducted on NER for English and Hindi, the complexities of Indian languages present unique challenges that require customized solutions. Working with NER for Indian languages is a difficult endeavor with limited resources available. This article provides a comprehensive review of NER approaches tailored for Indian languages. Indian languages pose unique challenges to NER due to their rich morphological and syntactic variations, script diversity and limited annotated data availability. This paper reviews the various techniques and methodologies employed in NER for Indian languages, including rule-based, machine learning and deep learning approaches. It analyzes the strengths and limitations of each approach. Additionally, this article examines the recent advancements in transfer learning and multilingual models, showcasing their potential in improving NER performance across Indian languages. This paper aims to guide researchers and practitioners in the development of NER systems for Indian languages and foster further advancements in this field. This article also provides a comprehensive review of the diverse approaches employed for NER in Indian languages, highlighting the strength and limitations as well.

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Section: (B) Machine Learning Approachmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

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State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

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“…This task, known as text summarization, requires selecting the most essential and salient concepts, entities, and relationships from the knowledge graph, and generating a brief and coherent summary of them. Text summarization can generally be divided into two categories: i) extractive summarization [14], which involves selecting the most salient and informative sentences from a document to create a summary, and ii) abstractive summarization [15], which involves generating a new summary that conveys the main ideas of the original document, potentially using new phrases and sentences that were not present in the original text. Our study focuses on the latter for generating abstractive summaries of DBPEDIA abstracts.…”

Section: Related Workmentioning

confidence: 99%

Using Pre-Trained Language Models for Abstractive DBPEDIA Summarization: A Comparative Study

Zahera,

Vitiugin,

Sherif

et al. 2023

Knowledge Graphs: Semantics, Machine Learning, and Languages

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Purpose: This study addresses the limitations of current short abstracts of DBPEDIA entities, which often lack a comprehensive overview due to their creating method (i.e., selecting the first two-three sentences from the full DBPEDIA abstracts). Methodology: We leverage pre-trained language models to generate abstractive summaries of DBPEDIA abstracts in six languages (English, French, German, Italian, Spanish, and Dutch). We performed several experiments to assess the quality of generated summaries by language models. In particular, we evaluated the generated summaries using human judgments and automated metrics (Self-ROUGE and BERTScore). Additionally, we studied the correlation between human judgments and automated metrics in evaluating the generated summaries under different aspects: informativeness, coherence, conciseness, and fluency. Findings: Pre-trained language models generate summaries more concise and informative than existing short abstracts. Specifically, BART-based models effectively overcome the limitations of DBPEDIA short abstracts, especially for longer ones. Moreover, we show that BERTScore and ROUGE-1 are reliable metrics for assessing the informativeness and coherence of the generated summaries with respect to the full DBPEDIA abstracts. We also find a negative correlation between conciseness and human ratings. Furthermore, fluency evaluation remains challenging without human judgment. Value: This study has significant implications for various applications in machine learning and natural language processing that rely on DBPEDIA resources. By providing succinct and comprehensive summaries, our approach enhances the quality of DBPEDIA abstracts and contributes to the semantic web community.

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“…Techniques such as sequence-to-sequence models, attention mechanisms, and neural embeddings have substantially improved the performance of summarization systems [8,9]. Despite these advancements, the challenge of integrating and leveraging diverse contextual relationships within a document remains a significant hurdle [10,11].…”

Section: Introductionmentioning

confidence: 99%

Contextual Hypergraph Networks for Enhanced Extractive Summarization: Introducing Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES)

Onan,

Alhumyani

2024

Applied Sciences

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Extractive summarization, a pivotal task in natural language processing, aims to distill essential content from lengthy documents efficiently. Traditional methods often struggle with capturing the nuanced interdependencies between different document elements, which is crucial to producing coherent and contextually rich summaries. This paper introduces Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES), a novel framework designed to address these challenges through an advanced hypergraph-based approach. MCHES constructs a contextual hypergraph where sentences form nodes interconnected by multiple types of hyperedges, including semantic, narrative, and discourse hyperedges. This structure captures complex relationships and maintains narrative flow, enhancing semantic coherence across the summary. The framework incorporates a Contextual Homogenization Module (CHM), which harmonizes features from diverse hyperedges, and a Hypergraph Contextual Attention Module (HCA), which employs a dual-level attention mechanism to focus on the most salient information. The innovative Extractive Read-out Strategy selects the optimal set of sentences to compose the final summary, ensuring that the latter reflects the core themes and logical structure of the original text. Our extensive evaluations demonstrate significant improvements over existing methods. Specifically, MCHES achieves an average ROUGE-1 score of 44.756, a ROUGE-2 score of 24.963, and a ROUGE-L score of 42.477 on the CNN/DailyMail dataset, surpassing the best-performing baseline by 3.662%, 3.395%, and 2.166% respectively. Furthermore, MCHES achieves BERTScore values of 59.995 on CNN/DailyMail, 88.424 on XSum, and 89.285 on PubMed, indicating superior semantic alignment with human-generated summaries. Additionally, MCHES achieves MoverScore values of 87.432 on CNN/DailyMail, 60.549 on XSum, and 59.739 on PubMed, highlighting its effectiveness in maintaining content movement and ordering. These results confirm that the MCHES framework sets a new standard for extractive summarization by leveraging contextual hypergraphs for better narrative and thematic fidelity.

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State-of-the-art approach to extractive text summarization: a comprehensive review

Cited by 21 publications

References 157 publications

State-of-art approach for Indian Language based on NER: Comprehensive Review

State-of-art approach for Indian Language based on NER: Comprehensive Review

Using Pre-Trained Language Models for Abstractive DBPEDIA Summarization: A Comparative Study

Contextual Hypergraph Networks for Enhanced Extractive Summarization: Introducing Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES)

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