Automatic Text Summarization Using a Machine Learning Approach

Neto, Joel Larocca; Freitas, Alex A.; Kaestner, Celso A. A.

doi:10.1007/3-540-36127-8_20

Cited by 148 publications

(103 citation statements)

References 12 publications

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“…Machine Learning (ML) approach can be applied if we have a set of training document and their corresponding summary extracts (Neto et al, 2002). The objective of machine learning can be closely related to a classification problem, i.e., to learn from a training model in order to determine the appropriate class where an element belongs to.…”

Section: Machine Learning Approachmentioning

confidence: 99%

“…Aone et al (1999) include feature like tf-idf using noun words and named entities, where they used the corpus consisting of news documents for their experiments. Another extensive investigation using the similar framework was carried by Neto et al (2002). The authors employ a large variety of features, including both statistical and linguistic features.…”

Section: A Naive Bayesmentioning

confidence: 99%

See 1 more Smart Citation

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: Machine Learning Approachmentioning

confidence: 99%

Section: A Naive Bayesmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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“…The Classification System was proposed by Larocca Neto et al [10] and uses a ML approach to determine relevant segments to extract from source texts. Actually, it is based on a Naïve Bayes classifier.…”

Section: The Classsumm Systemmentioning

confidence: 99%

“…ClassSumm was evaluated on a TIPSTER corpus of 100 news stories for training, and two test procedures, namely, one that has used 100 automatic summaries and another that has used 30 manual extracts [10], in which it outperforms the "from-top" -those from the beginning of the source text, and random order baselines.…”

Section: The Classsumm Systemmentioning

confidence: 99%

Advances in Artificial Intelligence – SBIA 2004

Rodrigues

Labidi²,

Russo³

2004

Lecture Notes in Computer Science

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Abstract. Automatic Summarization (AS) in Brazil has only recently become a significant research topic. When compared to other languages initiatives, such a delay can be explained by the lack of specific resources, such as expressive lexicons and corpora that could provide adequate foundations for deep or shallow approaches on AS. Taking advantage of having commonalities with respect to resources and a corpus of texts and summaries written in Brazilian Portuguese, two NLP research groups have decided to start a common task to assess and compare their AS systems. In the experiment five distinct extractive AS systems have been assessed. Some of them incorporate techniques that have been already used to summarize texts in English; others propose novel approaches to AS. Two baseline systems have also been considered. An overall performance comparison has been carried out, and its outcomes are discussed in this paper.

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“…Only a few have tried using machine learning to accomplish this difficult task (Lin 1999;Aone et al 1999;Neto et al 2002). Most research falls into combining statistical methods with linguistic analysis.…”

Section: Introductionmentioning

confidence: 99%