News Filtering and Summarization on the Web

Wu, Xindong; Wu, Gongqing; Xie, Fei; Zhu, Zhu; Hu, Xuegang; Lu, Hao; Li, Huiqian

doi:10.1109/mis.2010.11

Cited by 30 publications

(11 citation statements)

References 7 publications

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“…In order to compare our approach with state-of-the-art keyphrase extraction systems we have selected KEA [2] and KESR [25]. Most existing systems identify candidate phrases by the method applied in KEA and KESR.…”

Section: Experiments Results and Evaluationmentioning

confidence: 99%

Keyphrase Extraction of News Web Pages

Arya¹,

Dwivedi²

2018

IJEME

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Keyphrase extraction from news web pages is an important task for news documents retrieval and summarization. Keyphrases are like index terms that enclose the important information about document content. Keyphrases actually offer concise and precise description of document content. Key phrases are considered as a single word or a combination of more than one word that represent the important concepts in a text documents. The aim of this paper is to develop and evaluate an automatic keyphrases extraction approach for news web pages. Our approach identifies the candidate keyphrases from documents and chooses those candidate keyphrase having highest weight score. Weight formula combines the feature set that includes TF*IDF, phrase disatnce in documents and lexical chain that is based on WordNet to represent semantic relations between words. The experimental results show that the performance of our approach is better than the contemporary approaches today.

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Section: Experiments Results and Evaluationmentioning

confidence: 99%

Keyphrase Extraction of News Web Pages

Arya¹,

Dwivedi²

2018

IJEME

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“…A news filtering and summarization system is proposed in [3] which can automatically recognize web news pages. An automatic recognition method is used which applies classification rules for Web news based on a combination of URL, structure, and content attributes.…”

Section: Related Workmentioning

confidence: 99%

A summarizer system based on a semantic analysis of web documents

Florence¹,

Padmadas²

2015

2015 International Conference on Technologies for Sustainable Development (ICTSD)

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The availability of web and search engines has made the search easier nowadays. Information overload is one of the major problems which require algorithms and tools for faster access. Electronic documents are one of the major sources of information for business and academic information. In order to fully utilizing these on-line documents effectively, it is crucial to be able to extract the summary of these documents. Summarization system will be one of the solutions to the above problem. This project proposes a summarizer system which will be able to perform summarization of multiple documents. The input text documents are analyzed through a parser which parses the input documents and generates parse tree for each sentence. RDF triples are extracted from each sentence by analyzing the typed dependencies generated from the parser in the form of subject, verb and object. Semantic distance is computed between each pair of sentences and a matrix containing the semantic distance for sentences are computed. The measure adopted to compute semantic distance is Wu and Palmer distance. A clustering algorithm is applied to the extracted subject, verb and object space and the extracted RDF triples are grouped into clusters. The important sentences are selected for final summary are extracted using sentence selection algorithm.

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“…Wu et al presented a news filtering and summarization (NFAS) system that works on news Web pages [22]. The NFAS system consists of two main tasks.…”

Section: B Web News Extractionmentioning

confidence: 99%

“…According to the word similarity and lexical chains reviewed in Section II, our keyword extraction algorithm KLC (Keyword extraction based on Lexical Chains) is designed as follows, based on our KESR algorithm in the NFAS system [22].…”

Section: A Keyword Extraction Algorithmmentioning

confidence: 99%

Personalized News Filtering and Summarization on the Web

Xie

et al. 2011

2011 IEEE 23rd International Conference on Tools With Artificial Intelligence

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Abstract-Information on the World Wide Web is congested with large amounts of news contents. Recommendation, filtering, and summarization of Web news have received much attention in Web intelligence, aiming to find interesting news and summarize concise content for users. In this paper, we present our research on developing the Personalized News Filtering and Summarization system (PNFS). An embedded learning component of PNFS induces a user interest model and recommends personalized news. A keyword knowledge base is maintained and provides a real-time update to reflect the general Web news topic information and the user's interest preferences. The non-news content irrelevant to the news Web page is filtered out. Keywords that capture the main topic of the news are extracted using lexical chains to represent semantic relations between words. An Example run of our PNFS system demonstrates the superiority of this Web intelligence system.

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News Filtering and Summarization on the Web

Cited by 30 publications

References 7 publications

Keyphrase Extraction of News Web Pages

Keyphrase Extraction of News Web Pages

A summarizer system based on a semantic analysis of web documents

Personalized News Filtering and Summarization on the Web

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