Query-based multi-documents summarization using linguistic knowledge and content word expansion

Abdi, Asad; Idris, Norisma; Alguliyev, Rasim; Alıguliyev, Ramiz M.

doi:10.1007/s00500-015-1881-4

Cited by 27 publications

(14 citation statements)

References 40 publications

(43 reference statements)

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“…They examined the effectiveness of this proposed method and gave better result over classification model and learning-to-rank model. Abdi et al [1] used linguistic knowledge and content word expansion technique to find important information from text documents on the basis of users' requirements. The proposed method (QSLK) uses both word order and semantic similarity score for finding query-based text summarization.…”

Section: Literature Reviewmentioning

confidence: 99%

Improvement of query-based text summarization using word sense disambiguation

Rahman

Borah

2019

Complex Intell. Syst.

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In this paper, a query-based text summarization method is proposed based on common sense knowledge and word sense disambiguation. Common sense knowledge is integrated here by expanding the query terms. It helps in extracting main sentences from text document according to the query. Query-based text summarization finds semantic relatedness score between query and input text document for extracting sentences. The drawback with current methods is that while finding semantic relatedness between input text and query, in general they do not consider the sense of the words present in the input text sentences and the query. However, this particular method can enhance the summary quality as it finds the correct sense of each word of a sentence with respect to the context of the sentence. The correct sense for each word is being used while finding semantic relatedness between input text and query. To remove similar sentences from summary, similarity measure is computed among the selected sentences. Experimental result shows better performance than many baseline systems.

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Section: Literature Reviewmentioning

confidence: 99%

Improvement of query-based text summarization using word sense disambiguation

Rahman

Borah

2019

Complex Intell. Syst.

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“…Sentence relevance to the title is intended to determine the sentence's level of connectivity to the title in terms of both word-based and meaning-based similarity. Two sentences are considered similar or relevant if most of the words are the same or if they are a paraphrase of each other [14]. News features with grammatical information and sentence relevance to the title approach can be a great combination to find important sentences.…”

Section: Introductionmentioning

confidence: 99%

Feature-based POS tagging and sentence relevance for news multi-document summarization in Bahasa Indonesia

Abdullah

Fatichah

2022

Bulletin EEI

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Sentence extraction in news document summarization determines representative sentences primarily by employing the news feature known as news feature score (NeFS). NeFS can achieve meaningful sentences by analyzing the frequency and similarity of phrases while neglecting grammatical information and sentence relevance to the title. The presence of instructive content is indicated by grammatical information carried by part of speech (POS). POS tagging is the process of giving a meaningful tag to each term based on qualified data and even surrounding words. Sentence relevance to the title is intended to determine the sentence's level of connectivity to the title in terms of both word-based and meaning-based similarity, primarily for news documents in Bahasa Indonesia. In this study, we present an alternative sentence weighting method by incorporating news features, POS tagging, and sentence relevance to the title. Sentence extraction based on news features, POS tagging, and sentence relevance is introduced to extract the representative sentences. The experiment results on the 11 groups of Indonesian news documents are compared with the news features scores with the grammatical information approach method (NeFGIS). The proposed method achieved better results. The increasing f-score rate of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 sequentially are 1.84%, 3.03%, 3.85%, 2.08%.

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“…Summarization can be generic or query-based. The generic summary is about the whole text while the query-based summary is about the query being asked [2]. In terms of the number of texts, the summarization is divided into two categories: single document summarization (SDS) and multi-document summarization (MDS).…”

Section: -Introductionmentioning

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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Query-based multi-documents summarization using linguistic knowledge and content word expansion

Cited by 27 publications

References 40 publications

Improvement of query-based text summarization using word sense disambiguation

Improvement of query-based text summarization using word sense disambiguation

Feature-based POS tagging and sentence relevance for news multi-document summarization in Bahasa Indonesia

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

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