A topic modeling based approach to novel document automatic summarization

Wu, Zongda; Li, Lei; Li, Guiling; Huang, Hui; Zheng, Chengren; Chen, Enhong; Xu, Guandong

doi:10.1016/j.eswa.2017.04.054

Cited by 69 publications

(19 citation statements)

References 24 publications

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“…Most of the current summarization models can be categorized into two main categories, extractive-based and abstractive-based. The extractive-based ones are the most common, in which important words/sentences are extracted from text documents, and then recombined to form a summary [11]. In the following subsections, we introduce some recent related extractive and abstractive works with more attention to the works that are higher relevant to the study of this paper, including LSA based approaches, embedding-based approaches, and deep learning-based ones.…”

Section: Related Workmentioning

confidence: 99%

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Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings

2019

KSII TIIS

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Since the amount of information on the internet is growing rapidly, it is not easy for a user to find relevant information for his/her query. To tackle this issue, the researchers are paying much attention to Document Summarization. The key point in any successful document summarizer is a good document representation. The traditional approaches based on word overlapping mostly fail to produce that kind of representation. Word embedding has shown good performance allowing words to match on a semantic level. Naively concatenating word embeddings makes common words dominant which in turn diminish the representation quality. In this paper, we employ word embeddings to improve the weighting schemes for calculating the Latent Semantic Analysis input matrix. Two embedding-based weighting schemes are proposed and then combined to calculate the values of this matrix. They are modified versions of the augment weight and the entropy frequency that combine the strength of traditional weighting schemes and word embedding. The proposed approach is evaluated on three English datasets, DUC 2002, DUC 2004 and Multilingual 2015 Single-document Summarization. Experimental results on the three datasets show that the proposed model achieved competitive performance compared to the state-of-the-art leading to a conclusion that it provides a better document representation and a better document summary as a result.

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Section: Related Workmentioning

confidence: 99%

“…, where ( , ) refers to the similarity between word and sentence , calculated using Equation 7, and ( , ) refers to the similarity score of word with respect to the entire document calculated using Equation (11).…”

Section: B) Embedding-based Weighting Scheme (Embawef)mentioning

confidence: 99%

Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings

2019

KSII TIIS

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“…The researchers have been striving to utilize any advancement in NLP to create a more efficient summary. Extractive summarization techniques are the most common ones, whose basic idea is to extract important sentences from text documents and then recombine them to form a summary [14]. Recently, several summarization models have been proposed.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

et al. 2018

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The fast-growing amount of information on the Internet makes the research in automatic document summarization very urgent. It is an effective solution for information overload. Many approaches have been proposed based on different strategies, such as latent semantic analysis (LSA). However, LSA, when applied to document summarization, has some limitations which diminish its performance. In this work, we try to overcome these limitations by applying statistic and linear algebraic approaches combined with syntactic and semantic processing of text. First, the part of speech tagger is utilized to reduce the dimension of LSA. Then, the weight of the term in four adjacent sentences is added to the weighting schemes while calculating the input matrix to take into account the word order and the syntactic relations. In addition, a new LSA-based sentence selection algorithm is proposed, in which the term description is combined with sentence description for each topic which in turn makes the generated summary more informative and diverse. To ensure the effectiveness of the proposed LSA-based sentence selection algorithm, extensive experiment on Arabic and English are done. Four datasets are used to evaluate the new model, Linguistic Data Consortium (LDC) Arabic Newswire-a corpus, Essex Arabic Summaries Corpus (EASC), DUC2002, and Multilingual MSS 2015 dataset. Experimental results on the four datasets show the effectiveness of the proposed model on Arabic and English datasets. It performs comprehensively better compared to the state-of-the-art methods.

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“…In this method, highly connected node represents central sentences and may indicate the main topic of the document (Ferreira et al, 2014b;Oliveira et al, 2016). Wu et al (2017) proposed topic-modelling-based approach for long length single document Novel summarisation. They used external resources like SemCor and synonym thesaurus to solve the issue of semantic confusion to smoothen the summary readability.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Patel

Chhinkaniwala

2018

IJKEDM

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Text summarisation is compressed or condensed version of any text document. Due to increasing use of digitisation, massive amount of information is available on internet. Text summarisation is an emerging alternative for users to find relative information in automated shortened versions. In this paper we propose single document summarisation technique using shallow features of sentence to generate summary. The weight of sentences is calculated by applying score of different words and sentence-based statistical features. Here, most salient sentences are selected based on weight of sentences and are put together to generate summary. This is modeled using fuzzy inference system. This approach utilises fuzzy inference and fuzzy measures to find most significant sentences. The result of our proposed method is compared with other methods using recall oriented understudy for Gisting evaluation (ROUGE-N) measures on document understanding conferences (DUC) 2002 dataset and results show that our proposed method outperforms a few baseline methods.

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A topic modeling based approach to novel document automatic summarization

Cited by 69 publications

References 24 publications

Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings

Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings

An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

Fuzzy logic-based single document summarisation with improved sentence scoring technique

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