Multi-Document Text Summarization Using Clustering Techniques and Lexical Chaining

Saraswathi, S.; Arti, R.

doi:10.21917/ijsc.2010.0004

Cited by 4 publications

(1 citation statement)

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“…Text summarization is the process of extracting the most significant information from a source to produce a condensed version suitable for a specific audience or purpose (Saraswathi & Arti, 2010). Due to the Internet's swift expansion, it has become increasingly difficult to efficiently access the vast quantities of information available.…”

Section: Introductionmentioning

confidence: 99%

Using Manual and Automatic Summarization: What Should Students Consider in Writing a Summary?

Fitria

2023

electrum

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Writing a summary related to reading and writing. Writing a summary of a text not only helps students assimilate what they have read by highlighting and connecting the key points but also enables them to articulate their thoughts in writing. It means that a summary must be brief, accurate, and written in individual words. This study describes teaching summarization both manual and automatic summarization for students. This research is library research. The analysis shows that writing and summarizing a journal article is a common task for students. Students can read articles for summaries, plan good summaries, and write summaries to completion. Writing a summary can be manual or online (automatic) summarization. Manually, students need to consider several points before writing a summary including avoiding using personal pronouns, making sentences as objective as possible, beginning by defining the problem statement, discussing the author’s methodology, describing the research results, connecting the main ideas featured, do not conclude, provide the interpretation, avoid using direct quotes from journal articles, use appropriate tenses and improve students writing design. Students must check their self-evaluation of summarising skills, such as expressing the central idea or theme as a statement/declarative, assessing the quantity, quality, and order of the evidence, constructing evidence for inferential reasoning, paraphrasing details in their words, and avoiding common pitfalls. While automatic summarisation involved an online summarizer tool. Many summarization tools are available for English languages with abundant resources. However, students need to consider the benefits and the limitations.

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Section: Introductionmentioning

confidence: 99%

Using Manual and Automatic Summarization: What Should Students Consider in Writing a Summary?

Fitria

2023

electrum

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Binary Particle Swarm Optimization with an improved genetic algorithm to solve multi-document text summarization problem of Hindi documents

Aote¹,

Pimpalshende

Potnurwar³

et al. 2023

Engineering Applications of Artificial Intelligence

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An Automatic Multidocument Text Summarization Approach Based on Naïve Bayesian Classifier Using Timestamp Strategy

Ramanujam

Kaliappan²

2016

The Scientific World Journal

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Nowadays, automatic multidocument text summarization systems can successfully retrieve the summary sentences from the input documents. But, it has many limitations such as inaccurate extraction to essential sentences, low coverage, poor coherence among the sentences, and redundancy. This paper introduces a new concept of timestamp approach with Naïve Bayesian Classification approach for multidocument text summarization. The timestamp provides the summary an ordered look, which achieves the coherent looking summary. It extracts the more relevant information from the multiple documents. Here, scoring strategy is also used to calculate the score for the words to obtain the word frequency. The higher linguistic quality is estimated in terms of readability and comprehensibility. In order to show the efficiency of the proposed method, this paper presents the comparison between the proposed methods with the existing MEAD algorithm. The timestamp procedure is also applied on the MEAD algorithm and the results are examined with the proposed method. The results show that the proposed method results in lesser time than the existing MEAD algorithm to execute the summarization process. Moreover, the proposed method results in better precision, recall, and F-score than the existing clustering with lexical chaining approach.

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Multi-Document Text Summarization Using Clustering Techniques and Lexical Chaining

Cited by 4 publications

References 6 publications

Using Manual and Automatic Summarization: What Should Students Consider in Writing a Summary?

Using Manual and Automatic Summarization: What Should Students Consider in Writing a Summary?

Binary Particle Swarm Optimization with an improved genetic algorithm to solve multi-document text summarization problem of Hindi documents

An Automatic Multidocument Text Summarization Approach Based on Naïve Bayesian Classifier Using Timestamp Strategy

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