Generating succinct titles for web URLs

Chakrabarti, Deepayan; Kumar, Ravi; Punera, Kunal

doi:10.1145/1401890.1401905

Cited by 9 publications

(6 citation statements)

References 16 publications

(14 reference statements)

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“…Chakrabarti et al took an extractive approach for title generation, which starts with URL tokens, HTML titles, keywords, and anchor text on incoming links etc. 9 Their approach combines information from external sources, and performs probabilistic parameter learning with a URL's HTML title, context/abstract, and vocabulary at the source level.…”

Section: Web News Extractionmentioning

confidence: 99%

Pnfs: Personalized Web News Filtering and Summarization

Xie

et al. 2013

Int. J. Artif. Intell. Tools

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Information on the World Wide Web is congested with large amounts of news contents. Recommending, filtering, and summarization of Web news have become hot topics of research in Web intelligence, aiming to find interesting news for users and give concise content for reading. This paper presents 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. Two Web news recommendation methods are proposed to keep tracking news and find topic interesting news for users. A keyword knowledge base is maintained and provides real-time updates to reflect the news topic information and the user's interest preferences. The non-news content irrelevant to the news Web page is filtered out. A keyword extraction method based on lexical chains is proposed that uses the semantic similarity and the relatedness degree to represent the semantic relations between words. Word sense disambiguation is also performed in the built lexical chains. Experiments on Web news pages and journal articles show that the proposed keyword extraction method is effective. An example run of our PNFS system demonstrates the superiority of this Web intelligence system.

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Section: Web News Extractionmentioning

confidence: 99%

Pnfs: Personalized Web News Filtering and Summarization

Xie

et al. 2013

Int. J. Artif. Intell. Tools

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“…In a slightly different context, Chakrabarti et al used a statistical model that exploited multiple sources of information to create link-titles for result URLs [2]. In addition to the content of the the web page indicated by the URL, the authors utilised del.icio.us tags, anchor tags and queries associated with the web page via click-through logs to generate 'quicklinks'.…”

Section: Related Workmentioning

confidence: 99%

“…If this inverse relationship is replicated in a larger study, it may prompt a re-evaluation of these techniques in favour of algorithms exploiting other sources of information (e.g. [2]). …”

Section: Rank Assigned By Experts 1-10 11-20mentioning

confidence: 99%

A section title authoring tool for clinical guidelines

Truran

Georg

Cavazza

et al. 2012

Proceedings of the 2012 ACM Symposium on Document Engineering

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Professional users of medical information often report difficulties when attempting to locate specific information in lengthy documents. Sometimes these difficulties can be attributed to poorly specified section titles which fail to advertise relevant content. In this paper we describe preliminary work on a software plug-in for a document engineering environment that will assist authors when they formulate section-level headings. We describe two different algorithms which can be used to generate section titles. We compare the performance of these algorithms and correlate our experimental results with an evaluation of title quality performed by domain experts.

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“…Previous research has investigated methods for extracting implicit queries for contextual advertising [3,27,39] and the automatic generation of titles and quick-links for web pages [12,13]. The goal of both tasks is to extract short phrases that are relevant to a given web page.…”

Section: Related Workmentioning

confidence: 99%

Pseudo test collections for learning web search ranking functions

Asadi

Metzler

Elsayed

et al. 2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Test collections are the primary drivers of progress in information retrieval. They provide yardsticks for assessing the effectiveness of ranking functions in an automatic, rapid, and repeatable fashion and serve as training data for learning to rank models. However, manual construction of test collections tends to be slow, labor-intensive, and expensive. This paper examines the feasibility of constructing web search test collections in a completely unsupervised manner given only a large web corpus as input. Within our proposed framework, anchor text extracted from the web graph is treated as a pseudo query log from which pseudo queries are sampled. For each pseudo query, a set of relevant and non-relevant documents are selected using a variety of webspecific features, including spam and aggregated anchor text weights. The automatically mined queries and judgments form a pseudo test collection that can be used for training ranking functions. Experiments carried out on TREC web track data show that learning to rank models trained using pseudo test collections outperform an unsupervised ranking function and are statistically indistinguishable from a model trained using manual judgments, demonstrating the usefulness of our approach in extracting reasonable quality training data "for free".

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Generating succinct titles for web URLs

Cited by 9 publications

References 16 publications

Pnfs: Personalized Web News Filtering and Summarization

Pnfs: Personalized Web News Filtering and Summarization

A section title authoring tool for clinical guidelines

Pseudo test collections for learning web search ranking functions

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