Abstractive Text Summarization based on Improved Semantic Graph Approach

Khan, Atif; Salim, Naomie; Farman, Haleem; Khan, M H; Jan, Bilal; Ahmad, Awais; Ahmed, Imran; Paul, Anand

doi:10.1007/s10766-018-0560-3

Cited by 59 publications

(28 citation statements)

References 34 publications

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“…regarding number of sentences. Abstractive Text Summarization based on Improved Semantic Graph Approach [4]. 2018 Khan, Atif Salim, Naomie Farman, Haleem Khan, Murad Jan, Bilal Ahmad, Awais Ahmed, Imran Paul, Anand [4].…”

Section: Ilp Extricate Lesser Substancementioning

confidence: 99%

Application of Different Algorithms to Optimize Abstractive Summarization

Bhumika*,

Srikanth

2020

IJRTE

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Summarization is used to extract most relevant content from a huge content. It can be extractive and abstractive. But different people may have different scope of relevance in different area. Content can be in the form of opinions, news, judgments and ideas etc. Extractive Summarization extracts most focusing content without any change in the original content. Abstractive summarization is a knowledgebase extraction with some modification in original content. In this paper first we discuss various algorithms for abstractive summarization then on the basis of merits of various algorithms we discuss, how various algorithm may help to optimize Abstractive Summarization.

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Section: Ilp Extricate Lesser Substancementioning

confidence: 99%

Application of Different Algorithms to Optimize Abstractive Summarization

Bhumika*,

Srikanth

2020

IJRTE

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“… Repetition or duplicity of the content  Arrangement of words  Different people have different meaning for the same content  Number of times a content repeated  Different people have different interest  Nosiness in content  Information diversity Methods for Abstractive summarization: 1. integer linear programming-based summarization framework [3]. [4]. [5].…”

Section: Challenges In Abstractive Summarizationmentioning

confidence: 99%

“…This method mostly focuses on frequency of sentences. For better results ranking algorithm is used [4].…”

Section: Improvedmentioning

confidence: 99%

Abstractive Summarization with NLP Makes Easier to Memorize

Bhumika*,

Srikanth

2020

IJRTE

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Summarization is used to extract most relevant content from a huge content. It can be extractive and abstractive. But different people may have different scope of relevance in different area. Content can be in the form of opinions, news, judgments and ideas etc. Extractive Summarization extracts most focusing content without any change in the original content. Abstractive summarization is a knowledgebase extraction with some modification in original content. In this paper first we discuss various algorithm for abstractive summarization then on the basis of merits of various algorithm discussed in conclusions, we propose a new algorithm for abstractive summarization with the help of NLP to generate shortcuts from the content that make easier to memorize the content.

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“…In particular, semantic-based approaches utilizing (semantic) graphs produce the desired summaries through the extraction of ontological and syntactical relations in text, mainly by reducing the graph or by locating its key concepts (Khan et al, 2018;Joshi et al, 2018;Moawad and Aref, 2012). Item-based solutions, on the other hand, employ the notion of information item (the smallest unit of coherent textual information such as subject, verb and object triplets) in order to generate the summary out of the top-rated sentences.…”

Section: Related Workmentioning

confidence: 99%

Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization

Kouris¹,

Alexandridis²,

Stafylopatis³

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a summary in generalized form. Subsequently, a methodology is proposed which transforms the aforementioned generalized summary into human-readable form, retaining at the same time important informational aspects of the original text and addressing the problem of out-of-vocabulary or rare words. The overall approach is evaluated on two popular datasets with encouraging results.

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Abstractive Text Summarization based on Improved Semantic Graph Approach

Cited by 59 publications

References 34 publications

Application of Different Algorithms to Optimize Abstractive Summarization

Application of Different Algorithms to Optimize Abstractive Summarization

Abstractive Summarization with NLP Makes Easier to Memorize

Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization

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