Semantic graph reduction approach for abstractive Text Summarization

Moawad, Ibrahim F.; Aref, Mostafa

doi:10.1109/icces.2012.6408498

Cited by 84 publications

(36 citation statements)

References 3 publications

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“…Extraction rules were used for content selection and generation patterns for summary generation. Moawad and Aref [28] in 2012 used semantic graph based method and represented text using rich semantic graph. They used weights of concepts for content selection and reduced semantic graph and domain ontology for summary generation.…”

Section: Related Workmentioning

confidence: 99%

Domain Independent Framework for Automatic Text Summarization

Meena

Gopalani

2015

Procedia Computer Science

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Due to the exponential growth of documents on internet, users want all the relevant data at one place without any hassle. This led to the growth of Automatic Text Summarization. For this purpose a number of methods have been proposed by researchers but no method is able to work on all domains of text documents. Some methods which work for News domain may fail in Medical domain to give efficient results. In this paper we proposed a domain independent framework for Automatic Text Summarization. The Process first categorises the source text and then it applies the respective category's optimal set of rules or weights or method. The major advantage of framework is that it can be applicable for both extractive and abstractive text summarization.

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Section: Related Workmentioning

confidence: 99%

Domain Independent Framework for Automatic Text Summarization

Meena

Gopalani

2015

Procedia Computer Science

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“…This work by itself may not generate an excellent summary because the identification of the prevalent factor is an immense requirement; therefore, adopting the fusion network to form the well-formed sentence is a complicated issue. Moawad, I. F., & Aref, M [8] presented an approach which uses reduction technique on Rich Semantic Graph (RSG) to create an abstractive summary of a document. The approach maps the whole document into a Rich Semantic Graph.…”

Section: Single-document Summarizationmentioning

confidence: 99%

Recent Trends in Deep Learning Based Abstractive Text Summarization

Rane*¹,

Govilkar²

2019

IJRTE

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With the rapid growth of cyberspace and the appearance of knowledge exploration era, good text summarization method is vital to reduce the large data. Text summarization is the mechanism of extracting the important information which gives us an overall abstract or summary of the entire document and also reduces the size of the document. It is open problem in Natural Language Processing (NLP) and a difficult work for humans to understand and generate an abstract manually while it have need of a accurate analysis of the document. Text Summarization has become an important and timely tool for assisting and interpreting text information. It is generally distinguished into: Extractive and Abstractive. The first method directly chooses and outputs the relevant sentences in the original document; on the other hand, the latter rewrites the original document into summary using NLP techniques. From these two methods, abstractive text summarization is laborious task to realize as it needs correct understanding and sentence amalgamation. This paper gives a brief survey of the distinct attempts undertaken in the field of abstractive summarization. It collectively summarizes the numerous technologies, difficulties and problem of abstractive summarization

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“…From the subgraph, the final summary sentences are generated. Ibrahim F. Moawad et al [7] presented a novel approach to create an abstractive summary for a single document using a rich semantic graph reducing technique. The approach summaries the input document by creating a rich semantic graph for the original document, reducing the generated graph, and then generating the abstractive summary from the reduced graph.…”

Section: Related Workmentioning

confidence: 99%

Automatic summarization of Malayalam documents using clause identification method

Sunitha¹,

Jaya²,

Ganesh³

2019

IJECE

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<span>Text summarization is an active research area in the field of natural language processing. Huge amount of information in the internet necessitates the development of automatic summarization systems. There are two types of summarization techniques: Extractive and Abstractive. Extractive summarization selects important sentences from the text and produces summary as it is present in the original document. Abstractive summarization systems will provide a summary of the input text as is generated by human beings. Abstractive summary requires semantic analysis of text. Limited works have been carried out in the area of abstractive summarization in Indian languages especially in Malayalam. Only extractive summarization methods are proposed in Malayalam. In this paper, an abstractive summarization system for Malayalam documents using clause identification method is proposed. As part of this research work, a POS tagger and a morphological analyzer for Malayalam words in cricket domain are also developed. The clauses from input sentences are identified using a modified clause identification algorithm. The clauses are then semantically analyzed using an algorithm to identify semantic triples - subject, object and predicate. The score of each clause is then calculated by using feature extraction and the important clauses which are to be included in the summary are selected based on this score. Finally an algorithm is used to generate the sentences from the semantic triples of the selected clauses which is the abstractive summary of input documents.</span>

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Semantic graph reduction approach for abstractive Text Summarization

Cited by 84 publications

References 3 publications

Domain Independent Framework for Automatic Text Summarization

Domain Independent Framework for Automatic Text Summarization

Recent Trends in Deep Learning Based Abstractive Text Summarization

Automatic summarization of Malayalam documents using clause identification method

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