Introduction to the Special Issue on Summarization

Radev, Dragomir; Hovy, Eduard; McKeown, Kathleen

doi:10.1162/089120102762671927

Cited by 411 publications

(197 citation statements)

References 28 publications

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“…According to Radev, Hovy and McKeown, extraction is the process of identifying important segments of a text; abstraction is the process of reformulating and fusing these important segments in novel terms; and compression is the process of trimming unimportant material [5].…”

Section: Automatic Text Summarization Methodsmentioning

confidence: 99%

A Survey to Text Summarization Methods for Turkish

Birant¹,

Koşaner²,

Aktaş³

2016

IJCA

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Nowadays, people deal with a huge amount of data, especially while they are surfing on internet. So, this makes the topic of automatic summarization is very important and in the forefront. In this paper, a review for text summarization methods in Turkish is presented. Brief summary of the methods used for automatic text summarization in the literature, and also brief definitions of summary, abstraction and automatic text summarization are given.

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Section: Automatic Text Summarization Methodsmentioning

confidence: 99%

A Survey to Text Summarization Methods for Turkish

Birant¹,

Koşaner²,

Aktaş³

2016

IJCA

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“…The work was done almost manual. After these early work lot of work done in this discipline some are available in [1,14,15], here in the next section we are presenting only work done in recent years.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

Yadav

Sharan

2015

International Journal of Information Retrieval Research

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Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. A hybrid model for a single text document summarization is being proposed. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures: sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. The idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison, five system summaries have been generated: Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score.

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“…A summary can improve the effectiveness of the reader in searching and finding the desired document [6]. However, a document generally do not have a summary for a summary of a document is time consuming and cost [7]. In addition, a summary document is not easy because a summary should be able to represent the whole of the contents of the document.…”

Section: Introductionmentioning

confidence: 99%

Document Summarization using TextRank and Semantic Network

Ashari¹,

Riasetiawan²

2017

IJISA

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Abstract-The research has implemented document summarizing system uses TextRank algorithms and Semantic Networks and Corpus Statistics. The use of TextRank allows extraction of the main phrases of a document that used as a sentence in the summary output. The TextRank consists of several processes, namely tokenization sentence, the establishment of a graph, the edge value calculation algorithms using Semantic Networks and Corpus Statistics, vertex value calculation, sorting vertex value, and the creation of a summary. Testing has done by calculating the recall, precision, and F-Score of the summary using methods ROUGE-N to measure the quality of the system output. The quality of the summaries influenced by the style of writing, the selection of words and symbols in the document, as well as the length of the summary output of the system. The largest value of the F-Score is 10% of the length ta of the document with the F-Score 0.1635 and 150 words with the F-Score 0.1623.

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Introduction to the Special Issue on Summarization

Cited by 411 publications

References 28 publications

A Survey to Text Summarization Methods for Turkish

A Survey to Text Summarization Methods for Turkish

Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

Document Summarization using TextRank and Semantic Network

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