Multi-Document Summarization Based on Sentence Clustering Improved Using Topic Words

Lukmana, Indra; Swanjaya, Daniel; Kurniawardhani, Arrie; Arifin, Agus Zainal; Purwitasari, Diana

doi:10.12962/j24068535.v12i2.a317

Cited by 5 publications

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“…In another study it was stated that document summarization methods can also be differentiated into generic summarization and query-based summarization [9,14]. In this study also explained that generic summarization is divided into two parts, namely supervised and unsupervised methods.…”

mentioning

confidence: 78%

“…𝑠 𝑖 ∈𝐷 (9) where L(x) is a loss function, C indicates weights to balance factors and |D| indicates the number of sentences in D. After the regression function f0 is learned, the results are used to provide an estimate of the importance of the new sentence s 𝑠𝑐𝑜𝑟𝑒(𝑠 ′ ) = 𝑓 0 (𝐹(𝑠 ′ )) = 𝑤 0 . 𝐹(𝑠 ′ ) + 𝑏 0 (10)…”

Section: Sentence Ranking Methods With Regressionmentioning

confidence: 99%

“…This is the background to the need for an automatic summarization system in a document. An Automatic Text Summarization is a computer-based device to produce text that is shorter than the original text but still holds the main points of the summarized text [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Regression model focused on query for multi documents summarization based on significance of the sentence position

et al. 2019

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Document summarization is needed to get the information effectively and efficiently. One method used to obtain the document summarization by applying machine learning techniques. This paper proposes the application of regression models to query-focused multi-document summarization based on the significance of the sentence position. The method used is the Support Vector Regression (SVR) which estimates the weight of the sentence on a set of documents to be made as a summary based on sentence feature which has been defined previously. A series of evaluations performed on a data set of DUC 2005. From the test results obtained summary which has an average precision and recall values of 0.0580 and 0.0590 for measurements using ROUGE-2, ROUGE 0.0997 and 0.1019 for measurements using the proposed regression-SU4. Model can perform measurements of the significance of the position of the sentence in the document well.

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confidence: 78%

Section: Sentence Ranking Methods With Regressionmentioning

confidence: 99%

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Regression model focused on query for multi documents summarization based on significance of the sentence position

et al. 2019

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“…In this study, the research method from Sarkar [12] was adopted and the framework by Lukmana [13] was also employed. There are 5 steps that are conducted to obtain final summaries: 1) text preprocessing, 2) sentence clustering for clustering sentences in the dataset, 3) cluster ordering to order the cluster by descending order, 4) sentence extraction for extracting the representative sentence from each cluster as shown in Figure 1…”

Section: Methodsmentioning

confidence: 99%

Sentence Extraction Based on Sentence Distribution and Part of Speech Tagging for Multi-Document Summarization

et al. 2018

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Automatic multi-document summarization needs to find representative sentences not only b y sentence distrib ution to select the most important sentence b ut also b y how informative a term is in a sentence. Sentence distrib ution is suitab le for ob taining important sentences b y determining frequent and well-spread words in the corpus b ut ignores the grammatical information that indicates instructive content. The presence or ab sence of informative content in a sentence can b e indicated b y grammatical information which is carried b y part of speech (POS) lab els. In this paper, we propose a new sentence weighting method b y incorporating sentence distrib ution and POS tagging for multi -document summarization. Similarity-based Histogram Clustering (SHC) is used to cluster sentences in the data set. Cluster ordering is b ased on cluster importance to determine the important clusters. Sentence extraction b ased on sentence distrib ution and POS tagging is introduced to extract the representative sentences from the ordered clusters. The results of the experiment on the Document Unde rstanding Conferences (DUC) 2004 are compared with those of the Sentence Distrib ution Method. Our proposed method achieved b etter results with an increasing rate of 5.41% on ROUGE-1 and 0.62% on ROUGE-2.

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“…Cara yang dapat digunakan untuk mengelola informasi digital adalah dengan pengelompokan dokumen. Hasil dari pengelompokan dapat dimanfaatkan untuk pencarian informasi [1], sistem pendukung keputusan [2], perbandingan review produk secara otomatis [3], dan peringkasan dokumen [4] [5].…”

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Reduksi Dimensi Fitur Menggunakan Algoritma Aloft Untuk Pengelompokan Dokumen

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Multi-Document Summarization Based on Sentence Clustering Improved Using Topic Words

Cited by 5 publications

References 12 publications

Regression model focused on query for multi documents summarization based on significance of the sentence position

Regression model focused on query for multi documents summarization based on significance of the sentence position

Sentence Extraction Based on Sentence Distribution and Part of Speech Tagging for Multi-Document Summarization

Reduksi Dimensi Fitur Menggunakan Algoritma Aloft Untuk Pengelompokan Dokumen

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