A classification-based summarisation model for summarising text documents

Hannah, M. Esther; Mukherjee, Saswati

doi:10.1504/ijict.2014.063217

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Hannah and Mukherjee (2014) proposed a trainable summarizer for classifying important sentences. The authors used a decision tree model which was trained to classify sentences as interesting sentence and not interesting sentence.…”

Section: B Neural Networkmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

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Section: B Neural Networkmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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show abstract

“…(Gupta & Lehal, 2010). Machine learning-based summarizers makes use of training data to learn "relevant" and "non-relevant" sentence patterns (Svore et al, 2007) (Burges et al, 2005) (Hannah & Mukherjee, 2014).…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval

Ayed

Biskri

Meunier

2021

International Journal of Information Retrieval Research

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In the context of big data and the 4.0 industrial revolution era, enhancing document/information retrieval frameworks efficiency to handle the ever‐growing volume of text data in an ever more digital world is a must. This article describes a double-stage system of document/information retrieval. First, a Lucene-based document retrieval tool is implemented, and a couple of query expansion techniques using a comparable corpus (Wikipedia) and word embeddings are proposed and tested. Second, a retention-fidelity summarization protocol is performed on top of the retrieved documents to create a short, accurate, and fluent extract of a longer retrieved single document (or a set of top retrieved documents). Obtained results show that using word embeddings is an excellent way to achieve higher precision rates and retrieve more accurate documents. Also, obtained summaries satisfy the retention and fidelity criteria of relevant summaries.

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