An approach to text summarization

Sankar, K. Siva; Sobha, L.

doi:10.3115/1572433.1572441

Cited by 19 publications

(7 citation statements)

References 13 publications

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“…Sentence clustering, lexical chain and WordNet can be used to perform semantic analysis of text (Pal and Saha, 2014;Zhang and Li, 2009;Wei et al, 2015). Some graph-based document clustering or ranking algorithms are also used to incorporate semantic role of information in text summary (Ferreira et al, 2014a;Yan and Wan, 2014;Sankar and Sobha, 2009). With the assumption that mutual influence between sentences and words can boost the sentence score, Fang et al (2017) proposed unsupervised graph-based word-sentence co-ranking model to convey the intrinsic status of words and sentences more accurately.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Patel

Chhinkaniwala

2018

IJKEDM

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Text summarisation is compressed or condensed version of any text document. Due to increasing use of digitisation, massive amount of information is available on internet. Text summarisation is an emerging alternative for users to find relative information in automated shortened versions. In this paper we propose single document summarisation technique using shallow features of sentence to generate summary. The weight of sentences is calculated by applying score of different words and sentence-based statistical features. Here, most salient sentences are selected based on weight of sentences and are put together to generate summary. This is modeled using fuzzy inference system. This approach utilises fuzzy inference and fuzzy measures to find most significant sentences. The result of our proposed method is compared with other methods using recall oriented understudy for Gisting evaluation (ROUGE-N) measures on document understanding conferences (DUC) 2002 dataset and results show that our proposed method outperforms a few baseline methods.

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Section: Related Workmentioning

confidence: 99%

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Patel

Chhinkaniwala

2018

IJKEDM

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“…The software uses the external tool WordNet [4] to abstract the generated summary. WordNet is a lexical database that groups words by semantic relations.…”

Section: General Descriptionmentioning

confidence: 99%

“…In [2] the concept of using the context and possible synonyms of a word to form a graph has been explored. We use a similar method, however the similarity scores are generated using the WordNet [4] similarity function, and then the scores of vertices of the graph are calculated using Equation [2]. This results in a procedure similar to that of TextRank [1], but operating on words, and hence informally referred to as WordRank.…”

Section: Related Workmentioning

confidence: 99%

Automatic text summarizer

Patil

Dalmia

Ansari³

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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In today's fast-growing information age we have an abundance of text, especially on the web. New information is constantly being generated. Often due to time constraints we are not able to consume all the data available. It is therefore essential to be able to summarize the text so that it becomes easier to ingest, while maintaining the essence and understandability of the information. We aim to design an algorithm that can summarize a document by extracting key text and attempting to modify this extraction using a thesaurus. Our main goal is to reduce a given body of text to a fraction of its size, maintaining coherence and semantics.

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“…• Summaries should be concise. These aspects are undoubtedly important, but a good summary should also consist of other aspects such as coverage, nonredundancy, cohesion, relevancy, and readability (Shareghi and Hassanabadi, 2008;Sankar and Sobha, 2009;Parveen et al, 2016). To incorporate all these aspects in a summary is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

A Review on Text Summarization Techniques

Verma¹,

Verma²

2020

JSR

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In recent years, an enormous amount of text data from diversified sources has been emerged day-by-day. This huge amount of data carries essential information and knowledge that needs to be effectively summarized to be useful. Hence, the main contribution of this paper is twofold. We first introduce some concepts related to extractive text summarization and then provide a systematic analysis of various text summarization techniques. In particular, some challenges in extractive summarization of single as well as multiple documents are introduced. The problems focus on the textual assessment and similarity measurement between the text documents are addressed. The challenges discussed are generic and applicable to every possible scenario in text summarization. Then, existing state-of-the-art of extractive summarization techniques are discussed that focus on the identified challenges.

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An approach to text summarization

Cited by 19 publications

References 13 publications

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Automatic text summarizer

A Review on Text Summarization Techniques

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