Evolutionary Algorithms for Extractive Automatic Text Summarization

Meena, Yogesh Kumar; Gopalani, Dinesh

doi:10.1016/j.procs.2015.04.177

Cited by 43 publications

(21 citation statements)

References 6 publications

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“…if f itInd i > f itBestInd i then for all i = 1..popSize do 14: retrieve best particle from neighborhood 15: update speed according to Equations (1)-(4) and (7) to binary and continuous part respectively 16: modify particle p i position according to Equations (6) and (8) 17: end for 18: until reaching maxIte iterations 19: return solution of best particle in population Output: The particle with the last best fitness value…”

Section: Fitness Function Designmentioning

confidence: 99%

“…Recent works consider the task of producing extractive summaries as an optimization problem, where one or more target functions are proposed to select the "best" phrases from the document to form part of the summary [6]. However, these papers consider a set of metrics that are defined a priori, and the selection of metrics is not part of the optimization process, as for example in [7].…”

Section: Introductionmentioning

confidence: 99%

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User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Monte

Lanzarini

Bariviera

et al. 2019

Entropy

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Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.

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Section: Fitness Function Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Monte

Lanzarini

Bariviera

et al. 2019

Entropy

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show abstract

“…Dalam Bahasa Indonesia, suatu kalimat harus memiliki minimal satu subjek dan satu predikat. Kalimat yang terlalu panjang ataupun terlalu pendek harus dihindari dalam sebuah ringkasan sehingga dibutuhkan batasan (threshold) panjang kalimat yang diproses dalam pembobotan kalimat (Meena & Gopalani, 2014) (Meena & Gopalani, 2015).…”

Section: Sentence Length Thresholdunclassified

“…TF-ISF merupakan fitur yang digunakan pada penelitian (Fachrurrozi, Yusliani, & Yoanita, 2013) (Meena & Gopalani, 2015 …”

Section: Term Frequency -Inverse Sentence Frequency (Tf-isf)unclassified

Peringkasan multi-dokumen berita berdasarkan fitur berita dan part of speech tagging

Abdullah

Fatichah

2018

register. jurnal. ilm. teknologi. sistem. inf.

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Peringkasan multi-dokumen berita berdasarkan fitur berita dan part of … http://doi.org/10.26594/register.v4i2.1251 © 2018 Register: Jurnal Ilmiah Teknologi Sistem Informasi. Semua hak cipta dilindungi undang-undang. News Feature Scoring (NeFS) merupakan metode pembobotan kalimat yang sering digunakan untuk melakukan pembobotan kalimat pada peringkasan dokumen berdasarkan fitur berita. Beberapa fitur berita diantaranya seperti word frequency, sentence position, Term Frequency-Inverse Document Frequency (TF-IDF), dan kemiripan kalimat terhadap judul. Metode NeFS mampu memilih kalimat penting dengan menghitung frekuensi kata dan mengukur similaritas kata antara kalimat dengan judul. Akan tetapi pembobotan dengan metode NeFS tidak cukup, karena metode tersebut mengabaikan kata informatif yang terkandung dalam kalimat. Kata-kata informatif yang terkandung pada kalimat dapat mengindikasikan bahwa kalimat tersebut penting. Penelitian ini bertujuan untuk melakukan pembobotan kalimat pada peringkasan multidokumen berita dengan pendekatan fitur berita dan informasi gramatikal (NeFGIS). Informasi gramatikal yang dibawa oleh part of speech tagging (POS Tagging) dapat menunjukkan adanya konten informatif. Pembobotan kalimat dengan pendekatan fitur berita dan informasi gramatikal diharapkan mampu memilih kalimat representatif secara lebih baik dan mampu meningkatkan kualitas hasil ringkasan. Pada penelitian ini terdapat 4 tahapan yang dilakukan antara lain seleksi berita, text preprocessing, sentence scoring, dan penyusunan ringkasan. Untuk mengukur hasil ringkasan menggunakan metode evaluasi Recall-Oriented Understudy for Gisting Evaluation (ROUGE) dengan empat varian fungsi yaitu ROUGE-1, ROUGE-2, ROUGE-L, dan ROUGE-SU4. Hasil ringkasan menggunakan metode yang diusulkan (NeFGIS) dibandingkan dengan hasil ringkasan menggunakan metode pembobotan dengan pendekatan fitur berita dan trending issue (NeFTIS). Metode NeFGIS memberikan hasil yang lebih baik dengan peningkatan nilai untuk fungsi recall pada ROUGE-1, ROUGE-2, ROUGE-L, dan ROUGE-SU4 secara berturut-turut adalah 20,37%, 33,33%, 1,85%, 23,14%.

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“…In equation (3), in order to calculate the weight of term T i , C-ALL-T i is the number of occurrences of the T i in the terms of document. C-S-T i is the number of sentences containing T i .…”

Section:  Sentence Weight Criteriamentioning

confidence: 99%

A Combination Method for Improving Text Summarization

Mashreghi¹,

Esmaeili²

2017

IJCA

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As the volume of online and electronic information increasingly has grown, quickly and accurately access to these important resources is a big challenge. Text analytics can help by transposing words and sentences in unstructured data into high-quality information. Text summarization is one of the applications of text mining, has been of interest to researchers. In addition to text summarization, using optimization algorithms can be influenced results. In this paper, has been presented a hybrid approach for English multi-document summarization. As name suggest, a text summarization system produces summary of original documents. Combination of text mining and optimization algorithms is main ways this research, to improve results and reduce redundancy in summary sentences and simultaneously summary sentences have the most relevant. Similarity measures are cosine and overlap. Using multi-objective particle swarm optimization algorithm improved results. The experimental results of the method on two data sets DUC2005 and DUC2007 show improvements in the three assessment criteria associated. Result of summarization about 3 percent in ROUGE-1, in ROUGE-2 at 2 percent and the benchmark ROUGE-SU for approximately 1.5% compared with the previous methods improves.

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Evolutionary Algorithms for Extractive Automatic Text Summarization

Cited by 43 publications

References 6 publications

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Peringkasan multi-dokumen berita berdasarkan fitur berita dan part of speech tagging

A Combination Method for Improving Text Summarization

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