Automatic Text Summarization: Past, Present and Future

Saggion, Horacio; Poibeau, Thierry

doi:10.1007/978-3-642-28569-1_1

Cited by 125 publications

(59 citation statements)

References 46 publications

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“…Evaluation of a summary is a difficult task because there is no ideal summary for a document or a collection of documents and the definition of a good summary is an open question to large extent [16]. It has been found that human summarizers have low agreement for evaluating and producing summaries.…”

Section: Discussionmentioning

confidence: 99%

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Text Summarization Techniques: A Brief Survey

Allahyari¹,

Pouriyeh²,

Assefi³

et al. 2017

ijacsa

401

175

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Abstract-In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

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Section: Discussionmentioning

confidence: 99%

“…There are a number of papers that provide extensive overviews of text summarization techniques and systems [13]- [16].…”

Section: Introductionmentioning

confidence: 99%

Text Summarization Techniques: A Brief Survey

Allahyari¹,

Pouriyeh²,

Assefi³

et al. 2017

ijacsa

401

175

View full text Add to dashboard Cite

show abstract

“…Since information can be collected from multiple sources, condensing these information is considered essential. Various types of multi document summarization methods have been developed by researchers (Nenkova and McKeown, 2012;Saggion and Poibeau, 2013). In this section we will focus on two popular methods i.e., cluster based method and graph based method (Gupta and Lehal, 2010;Haque et al, 2013).…”

Section: Multi Document Summarizationmentioning

confidence: 99%

“…Since then, various techniques have been successfully used to extract the important contents from text document to represent document summary (Gupta and Lehal, 2010;Nenkova and McKeown, 2011;Saggion and Poibeau, 2013). The aim of automatic text summarization is similar to the reason why we humans create summaries; i.e., to produce a shorter representation of the original text.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic text summarization systems can be categorized into several different types (Nenkova and McKeown, 2012;Saggion and Poibeau, 2013). The different dimensions of text summarization can be generally categorized based on its input type (single or multi document), purpose (generic, domain specific, or query-based) and output type (extractive or abstractive).…”

Section: Introductionmentioning

confidence: 99%

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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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Cognitive mechanisms in sensemaking: A qualitative user study

Zhang

Soergel

2019

Asso for Info Science & Tech

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Throughout an information search, a user needs to make sense of the information found to create an understanding. This requires cognitive effort that can be demanding. Building on prior sensemaking models and expanding them with ideas from learning and cognitive psychology, we examined the use of cognitive mechanisms during individual sensemaking. We conducted a qualitative user study of 15 students who searched for and made sense of information for business analysis and news writing tasks. Through the analysis of think‐aloud protocols, recordings of screen movements, intermediate work products of sensemaking, including notes and concept maps, and final reports, we observed the use of 17 data‐driven and structure‐driven mechanisms for processing new information, examining individual concepts and relationships, and detecting anomalies. These cognitive mechanisms, as the basic operators that move sensemaking forward, provide in‐depth understanding of how people process information to produce sense. Meaningful learning and sensemaking are closely related, so our findings apply to learning as well. Our results contribute to a better understanding of the sensemaking process—how people think—and this better understanding can inform the teaching of thinking skills and the design of improved sensemaking assistants and mind tools.

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Automatic Text Summarization: Past, Present and Future

Cited by 125 publications

References 46 publications

Text Summarization Techniques: A Brief Survey

Text Summarization Techniques: A Brief Survey

A Review on Automatic Text Summarization Approaches

Cognitive mechanisms in sensemaking: A qualitative user study

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