A Discussion on Various Methods in Automatic Abstractive Text Summarization

Karnik, Madhuri P.; Kodavade, D. V.

doi:10.1007/978-3-030-49795-8_51

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Abstractive summarization creates a summary by paraphrasing and rewriting the text using different words and grammar [ 24 ] and is similar to the manual summarization process used by human experts. The process is more complex than extractive summarization as it involves the use of deep language features and complex NLP processes [ 25 – 28 ]. ATS can be further classified based on the number of input documents into single-document summarization (SDS) and multi-document summarization (MDS).…”

Section: Introductionmentioning

confidence: 99%

Graph-based extractive text summarization method for Hausa text

Bichi,

Samsudin,

Hassan

et al. 2023

PLoS ONE

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Automatic text summarization is one of the most promising solutions to the ever-growing challenges of textual data as it produces a shorter version of the original document with fewer bytes, but the same information as the original document. Despite the advancements in automatic text summarization research, research involving the development of automatic text summarization methods for documents written in Hausa, a Chadic language widely spoken in West Africa by approximately 150,000,000 people as either their first or second language, is still in early stages of development. This study proposes a novel graph-based extractive single-document summarization method for Hausa text by modifying the existing PageRank algorithm using the normalized common bigrams count between adjacent sentences as the initial vertex score. The proposed method is evaluated using a primarily collected Hausa summarization evaluation dataset comprising of 113 Hausa news articles on ROUGE evaluation toolkits. The proposed approach outperformed the standard methods using the same datasets. It outperformed the TextRank method by 2.1%, LexRank by 12.3%, centroid-based method by 19.5%, and BM25 method by 17.4%.

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Section: Introductionmentioning

confidence: 99%

Graph-based extractive text summarization method for Hausa text

Bichi,

Samsudin,

Hassan

et al. 2023

PLoS ONE

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“…In the publication [1], a survey and observations of extractive and abstractive text summarization methods are described, and in the paper [2], a survey on several approaches to automatic abstractive text summarization is presented. In 2015 [3], abstractive text summarization used deep learning techniques for the first time, and the proposed model utilized an encoder-decoder design.…”

Section: Introductionmentioning

confidence: 99%

Abstractive Summarization with Efficient Transformer Based Approach

Karnik

Kodavade

2023

IJRITCC

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One of the most significant research areas is how to make a document smaller while keeping its essential information because of the rapid proliferation of online data. This information must be summarized in order to recover meaningful knowledge in an acceptable time. Text summarization is what it's called. Extractive and abstractive text summarization are the two types of summarization. In current years, the arena of abstractive text summarization has become increasingly popular. Abstractive Text Summarization (ATS) aims to extract the most vital content from a text corpus and condense it into a shorter text while maintaining its meaning and semantic and grammatical accuracy. Deep learning architectures have entered a new phase in natural language processing (NLP). Many studies have demonstrated the competitive performance of innovative architectures including recurrent neural network (RNN), Attention Mechanism and LSTM among others. Transformer, a recently presented model, relies on the attention process. In this paper, abstractive text summarization is accomplished using a basic Transformer model, a Transformer with a pointer generation network (PGN) and coverage mechanism, a Fastformer architecture and Fastformer with pointer generation network (PGN) and coverage mechanism. We compare these architectures after careful and thorough hyperparameter adjustment. In the experiment the standard CNN/DM dataset is used to test these architectures on the job of abstractive summarization.

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Text Summarization Using Lexical Chaining and Concept Generalization

Paul¹,

Salim²

2022

Intelligent Communication Technologies and Virtual Mobile Networks

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A Discussion on Various Methods in Automatic Abstractive Text Summarization

Cited by 3 publications

References 8 publications

Graph-based extractive text summarization method for Hausa text

Graph-based extractive text summarization method for Hausa text

Abstractive Summarization with Efficient Transformer Based Approach

Text Summarization Using Lexical Chaining and Concept Generalization

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