Selection Based Comparative Summarization of Search Results Using Concept Based segmentation

Pasupathi, Chitra; Baskaran, R.; Sarukesi, K.

doi:10.1007/978-3-642-22543-7_67

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Recently, a list of approaches have been reported to tackle the problem of comparative summarization [6,8,12,18,21]. These approaches can mainly be categorized into two types of strategies: (1) considering only the differences between documents; and (2) focusing on both commonalities and differences of documents.…”

Section: Existing Solutionsmentioning

confidence: 99%

PatentCom: A Comparative View of Patent Document Retrieval

Zhang

Shen

et al. 2015

Proceedings of the 2015 SIAM International Conference on Data Mining

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Section: Existing Solutionsmentioning

confidence: 99%

PatentCom: A Comparative View of Patent Document Retrieval

Zhang

Shen

et al. 2015

Proceedings of the 2015 SIAM International Conference on Data Mining

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“…Of the five major or "core" search engines, Google held a substantial lead over its rivals for more than the past five years (Pasupathi et al, 2011) (according to comScore research and Search Marketing Standard). Ebiz MBA Knowledge database statistics says that more than 9 billion monthly visitors are using Google for information search on WWW.…”

Section: Experimentation and Evaluationmentioning

confidence: 99%

Web Document Segmentation Using Frequent Term Sets for Summarization

Pasupathi¹,

Baskaran

Sarukesi

2012

Journal of Computer Science

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Query sensitive summarization aims at extracting the query relevant contents from web documents. Web page segmentation focuses on reducing the run time overhead of the summarization systems by grouping the related contents of a web page into segments. At query time, query relevant segments of the web page are identified and important sentences from these segments are extracted to compose the summary. DOM tree structures of the web documents are utilized to perform the segmentation of the contents. Leaf nodes of DOM tress are merged to form segments according to the statistical and linguistic similarity measure. The proposed system has been evaluated by intrinsic approach making use of user satisfaction index. The performance of the system is compared with summarization without using preprocessed segments. Performance of this system is more promising than the other measures like cosine similarity, jaccard measure which make use of sparse term-frequent vectors, since the most frequent term sets are considered to measure the relevance. Relevant segments alone need to be processed at run time for summarization which reduces the time complexity of the summarization process.

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“…Using this information, users have to decide which of the listed documents in the search result will be most likely to satisfy their information need. This paper is an extension of our work mentioned in [1], proposes a summarization technique, which extracts query relevant important sentences from a set of selected web pages to generate a comparative summary which would be beneficial for the users to make informed decisions. The remainder of this paper is organized as follows: Section 2 provides the motivating examples for this work, Section 3 discusses about the related research works that have been done in this field and Section 4 describes concept based segmentation process guided by the webpage's DOM tree structure.…”

Section: Introductionmentioning

confidence: 99%