The diversity-based approach to open-domain text summarization

Nomoto, Tadashi; Matsumoto, Yūji

doi:10.1016/s0306-4573(02)00096-1

Cited by 22 publications

(12 citation statements)

References 21 publications

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“…Whilst domain-restricted text summarization (Reeve et al, 2007;Ando et al, 2005) is easier due to the prominence of topics in the homogeneous document collection, open domain text summarization (Nomoto and Matsumoto, 2003) is more challenging, which is topped by the text expression variation challenges.…”

Section: Text Summarization Techniquesmentioning

confidence: 99%

Modelling Knowledge Summarization by Evolving Fuzzy Grammar

Sharef

Halin

Mustapha

2013

American Journal of Applied Sciences

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Summarized text is a simplified and condensed version of the original text containing highlighted information to help the audience get the gist in a short period of time. Typically, text summarization produces abstract or a paragraph-like outputs by omitting details and irrelevant information. However, the text summary can also be produced in a visualized form, such as a chart, graph or table representing a collection of similar cases. The visualized version generates a statistical-like presentation, which often involves numerical and ordinal observation of the gathered knowledge from the text. This requires lexical syntacticSummarized text is a simplified and condensed version of the original text containing highlighted information to help the audience get the gist in a short period of time. Typically, text summarization produces abstract or a paragraph-like outputs by omitting details and irrelevant information. However, the text summary can also be produced in a visualized form, such as a chart, graph or table representing a collection of similar cases. The visualized version generates a statistical-like presentation, which often involves numerical and ordinal observation of the gathered knowledge from the text. This requires lexical syntactic understanding of the text. Essential to achieve this goal is topic identification, message analysis/interpretation and knowledge summarization generation. The objective of this study is to model knowledge summarization problem using the evolving fuzzy grammar technique and we focus on metadata generation for producing visualized knowledge summarization. The process comprises of: (i) identifying the underlying structure of the texts for knowledge summarization, (ii) represent the identified knowledge for summarization manipulation and (iii) presentation of the summarized knowledge. A prototype called FTCatÂ© is developed as a proof of concept and we demonstrate its practicality in summarizing news reports. understanding of the text. Essential to achieve this goal is topic identification, message analysis/interpretation and knowledge summarization generation. The objective of this study is to model knowledge summarization problem using the evolving fuzzy grammar technique and we focus on metadata generation for producing visualized knowledge summarization. The process comprises of: (i) identifying the underlying structure of the texts for knowledge summarization, (ii) represent the identified knowledge for summarization manipulation and (iii) presentation of the summarized knowledge. A prototype called FTCatÂ© is developed as a proof of concept and we demonstrate its practicality in summarizing news reports

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Section: Text Summarization Techniquesmentioning

confidence: 99%

Modelling Knowledge Summarization by Evolving Fuzzy Grammar

Sharef

Halin

Mustapha

2013

American Journal of Applied Sciences

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“…For example, various authors have argued that incorporating sentence clustering into extractive multi-document summarization helps avoid problems of content overlap, leading to better coverage [31,55]. In [48], the clustering it is used as an effective tool for finding the diversity among the sentences. This work firstly clusters the sentences and uses the obtained sentence clusters to generate a summary.…”

Section: Related Workmentioning

confidence: 99%

DESAMC+DocSum: Differential evolution with self-adaptive mutation and crossover parameters for multi-document summarization

Alguliyev

Alıguliyev

Isazade

2012

Knowledge-Based Systems

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“…Its ability to automatically group similar textual objects together enables one to discover hidden similarity and key concepts, as well as to summarize a large amount of text into a small number of groups [14]. In [15], the clustering is used as an effective tool for finding the diversity among the sentences. This work firstly clusters the sentences and uses the obtained sentence clusters to generate a summary.…”

Section: Related Workmentioning

confidence: 99%

“…, n} into the interval (0, 1), which is equivalent to the interval of a probability function. After such transformation from the real-coded representation (15) we obtain the binary-coded representation, x p,i (t) ∈ {0, 1}, where the x p,i (t) = 1 indicates that the ith sentence is selected to be included to the summary, otherwise, the ith sentence will not be selected. For example, the individual X p (t) = [1, 0, 0, 1, 1] represents a candidate solution that first, fourth, and fifth sentences are selected to be included to the summary.…”

Section: Binarizationmentioning

confidence: 99%

pSum‐SaDE: A Modified p‐Median Problem and Self‐Adaptive Differential Evolution Algorithm for Text Summarization

Alguliyev

Alıguliyev

Mehdiyev

2011

Applied Computational Intelligence and Soft Computing

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Correspondence should be addressed to Ramiz M. Aliguliyev, r.aliguliyev@gmail.com Extractive multidocument summarization is modeled as a modified p-median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.

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The diversity-based approach to open-domain text summarization

Cited by 22 publications

References 21 publications

Modelling Knowledge Summarization by Evolving Fuzzy Grammar

Modelling Knowledge Summarization by Evolving Fuzzy Grammar

DESAMC+DocSum: Differential evolution with self-adaptive mutation and crossover parameters for multi-document summarization

pSum‐SaDE: A Modified p‐Median Problem and Self‐Adaptive Differential Evolution Algorithm for Text Summarization

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