A Clustered Semantic Graph Approach for Multi-Document Abstractive Summarization

Khan, Atif; Salim, Naomie; Reafee, Waleed; Sukprasert, Anupong; Kumar, Yogan Jaya

doi:10.11113/jt.v77.6491

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…Experiment of this study is performed using DUC-2002, a standard corpus for text summarization. Experimental results reveal that the proposed approach outperforms other summarization systems [4]…”

Section: Literature Reviewmentioning

confidence: 97%

Hybrid Method for Framing Abstractive Summaries of Tweets

Akkineni¹,

Papineni²,

Babu³

2017

IJIES

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Ubiquitous online communication is producing massive amounts of data on an un-precedential scale. Many have come up in analyzing the data produced from Twitter. Sentiment analysis is being carried out to catch the pulse of the people. As the data is mostly in the unstructured format. Getting the summary of what has been expressed positively and what has been expressed negatively plays a major role. Summaries take a wide strand in this plait as the relevant content can't be comprehended all at once. Working on the premise that online social media conversations might represent a new source of information to monitor the status of the policies launched by the government, this investigate stands as a first paw which may ultimately be the source for producing abstractive summaries from Twitter. This paper mainly focuses on developing a hybrid method which takes the combination of extractive summaries using statistical approaches as well as abstractive summaries using Rich Semantic Graph based approaches. The set of classified positive and the negative tweets will be passed as input to the hybrid method and it generates concise as well as readable summaries from both set of tweets. These summaries can be easily viewable on the PDA's and enhances the decision making capability of the policymaker.

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Section: Literature Reviewmentioning

confidence: 97%

Hybrid Method for Framing Abstractive Summaries of Tweets

Akkineni¹,

Papineni²,

Babu³

2017

IJIES

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“…This method extracts sentences that Coverage is guaranteed by ranking sentences by their degree of connection to other topic sentences and phrases. Khan et al present a clustered semantic graph approach for multi-document abstractive summarization [21]. Most of the existing graph-based methods rely on the bag of words method, which treats sentences as a bag of words and relies on a content similarity measure.…”

Section: -3-graph-based Approachesmentioning

confidence: 99%

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Afsharizadeh¹,

Ebrhimpour-Komeleh²,

Bagheri³

et al. 2022

jist

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Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decision. This is the problem of information overload. Multi-document summarization is an important solution for this problem by producing a brief summary containing the most important information from a set of documents in a short time. This summary should preserve the main concepts of the documents. When the input documents are related to a specific domain, for example, medicine or law, summarization faces more challenges. Domain-oriented summarization methods use special characteristics related to that domain to generate summaries. This paper introduces the purpose of multi-document summarization systems and discusses domain-oriented approaches. Various methods have been proposed by researchers for multi-document summarization. This survey reviews the categorizations that authors have made on multi-document summarization methods. We also categorize the multi-document summarization methods into six categories: machine learning, clustering, graph, Latent Dirichlet Allocation (LDA), optimization, and deep learning. We review the different methods presented in each of these groups. We also compare the advantages and disadvantages of these groups. We have discussed the standard datasets used in this field, evaluation measures, challenges and recommendations.

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“…Bagi menghasilkan sebuah ringkasan, kebanyakan model peringkasan yang sedia ada sering menggunakan kaedah perwakilan teks yang asas, iaitu menggunakan model bahasa tradisional seperti Bag-of-Words (BOW) bagi mewakili setiap istilah dalam teks yang dijadikan sebagai vektor kata kunci n-dimensi seperti yang terdapat dalam (Conroy, Schlesinger, O'leary, & Goldstein, 2006). Sementara itu, terdapat model-model peringkasan yang lain telah mengeksploitasi model kebarangkalian model N-gram sebagai fitur ayat (Clarke & Lapata, 2008) dan ada juga yang mewakilkan model N-gram tersebut dalam bentuk graf (Ganesan, Zhai, & Han, 2010;Khan, Salim, Reafee, Sukprasert, & Kumar, 2015;Van Lierde & Chow, 2019). Walau bagaimanapun, terdapat beberapa isu ketara di dalam model-model bahasa ini seperti perwakilan semantik yang kurang tepat dan maksud perkataan yang terpesong.…”

Section: Kaedah Perwakilan Teksunclassified

Model Peringkasan Teks Ekstraktif Dwibahasa menggunakan Fitur Kekangan Corak Tekstual (Bilingual Extractive Text Summarization Model using Textual Pattern Constraints)

Alias

Sainin

Mohammad

2020

gema

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In the era of digital information, an auto-generated summary can help readers to easily find important and relevant information. Most of the studies and benchmark data sets in the field of text summarization are in English. Hence, there is a need to study the potential of Malay language in this field. This study also highlights the problems in identifying and generating important information in extractive summaries. This is because existing text representation models such as BOW has weaknesses in inaccurate semantic representation, while the N-gram model has the issue of producing very high word vector dimensions. In this study, a bilingual text summarization model named MYTextSumBASIC has been developed to generate an extractive summary automatically in Malay and English. The MYTextSumBASIC summarizer model applies a text representation model known as FASP using three Textual Pattern Constraints, namely word item constraints, adjacent word constraints and sequence size constraints. There are three main phases in the framework of MYTextSumBASIC model, which are the development of the Malay language corpus, the development of MYTextSumBASIC model using FASP and the summary evaluation phase. In the summary evaluation phase, using the Malay language data sets of 100 news articles, the summaries produced by MYTextSumBASIC outperformed the summary generated by Baseline (Lead) and OTS summarizer with the highest average for retrieval (R) is 0.5849, precision (P) is 0.5736 and the F-score (Fm) is 0.5772. For manual evaluation by linguists, the MYTextSumBASIC method yielded a reading score of 4.1 and 3.87 for summary content generated using a random data set. Further experiments using the 2002 DUC English benchmark data set of 102 news articles have also shown that the MYTextSumBASIC model outperformed the best and lowest systems in the comparison with the mean retrieval values of ROUGE-1 (0.43896) and ROUGE-2 (0.19918). These findings conclude that the FASP text representation feature along with the textual pattern constraints used by our model can be used for bilingual text with competitive performance compared to other text summarization models.

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A Clustered Semantic Graph Approach for Multi-Document Abstractive Summarization

Cited by 6 publications

References 33 publications

Hybrid Method for Framing Abstractive Summaries of Tweets

Hybrid Method for Framing Abstractive Summaries of Tweets

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Model Peringkasan Teks Ekstraktif Dwibahasa menggunakan Fitur Kekangan Corak Tekstual (Bilingual Extractive Text Summarization Model using Textual Pattern Constraints)

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