Panoramic View System for extracting key sentences based on viewpoints and application to a search engine

Sunayama, Wataru; Yachida, Masahiko

doi:10.1016/j.jnca.2004.01.005

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…The evaluation was performed by feeding five types of text (described later) into a key sentence extraction system and comparing the output summaries. The key sentence extraction system used was the "Panoramic View System" [15], and since it extracts key sentences with respect to a search term, * it is a good match with our proposed system, which is intended to improve summarization for the Web page searches.…”

Section: Html Text Segmentation System and Web Page Summarizationmentioning

confidence: 99%

HTML text segmentation for Web page summarization by a key sentence extraction method

Sunayama

Iyama

Yachida

2006

Systems & Computers in Japan

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SUMMARYThe information displayed as the search result by search engines is important for quickly finding the desired information. In particular, the summary of each Web page in the search results is important for determining the Web page content, as well as for determining how the input search term is used in each Web page, namely, the relation between the search term and the Web page. However, the summaries of the search results in conventional search engines have problems such as extracting only the opening text and not containing the search term, or containing the search term but having the sentence truncated in the middle so that the context of the term or the content of the Web page cannot be determined. Therefore, a summary in sentence units is desirable, but since HTML text includes many nonsentence items that do not contain punctuation, if they are unprocessed, it is difficult for a key sentence extraction system that treats sentences as units to provide a summary. Thus, in this paper, we propose an HTML text segmentation system that divides the source text of each Web page into meaningfully connected groups of text corresponding to sentences. We also verify experimentally that the text generated by this system can be used effectively in a Web page summarization.

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Section: Html Text Segmentation System and Web Page Summarizationmentioning

confidence: 99%

HTML text segmentation for Web page summarization by a key sentence extraction method

Sunayama

Iyama

Yachida

2006

Systems & Computers in Japan

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“…This system divides the HTML text according to semantic intervals, lets one of those divided units be a sentence, and outputs a set of those sentences as a written text. As the key sentence extraction system, a panoramic view system was used, which enables a higher precision key sentence that combines high-and low-frequency words to be extracted by using the frequency of each word and the conditional probability based on the co-occurrence frequency of words in each sentence, rather than a conventional key sentence extraction system, which is likely to extract only a sentence containing high-frequency words as the key sentence [9]. This panoramic view system also enables a key sentence to be extracted based on the viewpoint of the person searching for information.…”

Section: Evaluation Module Based On Key Sentencesmentioning

confidence: 99%

Image labeling using key sentences of HTML

Sagara

Sunayama

Yachida

2006

Electron Comm Jpn Pt III

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SUMMARYThe word "multimedia" is commonly heard nowadays. The essential meaning of this word encompasses a broad range of contents. However, it is indisputable that information processing from now on will focus on visual information such as video and images, which are intuitively easy for us to understand and which carry a great deal of information. Therefore, in this paper, we propose an image labeling technique for supporting image retrieval on the Web by focusing on the relationship between images on Web pages and key sentences within related text areas and assigning labels to images based on these key sentences.

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“…Chang [3] used words clustering to generate cluster center which can be regarded as the topic concept. Sunayama [4] used the idea of the text automatic summarization assessing the importance of sentences to choose the most appropriate sentences as the topic sentences.…”

Section: Introductionmentioning

confidence: 99%

Research on Text Hierarchical Topic Identification Algorithm Based on the Dynamic Diverse Thresholds Clustering

Quan

et al. 2009

2009 International Conference on Asian Language Processing

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In many NLP applications, text topic identification is a common problem. Traditional topic identification method always generated a single-layered topic structure which is usually inaccurate topic division even if generated manually by the human experts. This paper proposed a concept of hierarchical topic which used multi-layer topic tree structure to represent the text or text set. Secondly, this paper proposed an iterative text units clustering method to recognize automatically the hierarchical topic of the text set. In this method, text clustering processing paused when each topic in the text set were correctly divided into multiple sub-topics, and such processing continued until a hierarchical topic tree had been built. A difficult problem of this method was how to automatically determine multiple pause threshold values and was resolved by the minimized clustering entropy method in this paper. The results of our experiments demonstrated the effectiveness of the method.

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Panoramic View System for extracting key sentences based on viewpoints and application to a search engine

Cited by 8 publications

References 8 publications

HTML text segmentation for Web page summarization by a key sentence extraction method

HTML text segmentation for Web page summarization by a key sentence extraction method

Image labeling using key sentences of HTML

Research on Text Hierarchical Topic Identification Algorithm Based on the Dynamic Diverse Thresholds Clustering

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