Multi-document summarization

About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.

Web content adaptation classification and summarization in mobile devices

Abstract: With the rapid growth of the small screen audience, the need for less cumbersome methods of browsing information on the Internet is growing too. Small screen devices provide portability in accessing huge information space on the Internet. It is unattainable to load and visualize large documents on hand held devices. Our objective is ultimately to provide mobile Internet users with a useful tool for web access. The paper proposes a model for adaptation of Web content and summarization and classification of text documents in specified categories. This paper describes the text classification and summarization based on user behavior and preferences. Experiments have been carried out on text database of 500 files and results are found to be quite satisfactory.

A Study of Chinese Text Summarization Based on Thematic Area Discovery

Abstract: Automatic summarization is an important issue in Natural Language Processing. This paper has proposed a special method that creates text summary by discovering thematic areas from Chinese text. The specificity of the method is that the created summary can both cover as many as different themes and reduce its redundancy obviously at the same time. And the discovery of latent thematic areas under the adaptive clustering of passages is realized by adopting k-medoids clustering method as well as a novel clustering analysis method based on self-defined objective function. In addition, a novel parameter,which is known as representation entropy,is used for summarization redun- dancy evaluation. Experimental results indicate that this method is effective and efficient in the automatic summariza- tion literature.

Combining Machine Learning and Natural Language Processing for Language-Specific, Multi-Lingual, and Cross-Lingual Text Summarization: A Wide-Ranging Overview

Abstract: The recent advances in multimedia and web-based applications have eased the accessibility to large collections of textual documents. To automate the process of document analysis, the research community has put relevant efforts into extracting short summaries of the document content. However, most of the early proposed summarization methods were tailored to English-written textual corpora or to collections of documents all written in the same language. More recently, the joint efforts of the machine learning and the natural language processing communities have produced more portable and flexible solutions, which can be applied to documents written in different languages. This chapter first overviews the most relevant language-specific summarization algorithms. Then, it presents the most recent advances in multiand cross-lingual text summarization. The chapter classifies the presented methodology, highlights the main pros and cons, and discusses the perspectives of the extension of the current research towards cross-lingual summarization systems. Combining Machine Learning and Natural Language Processing for Language-Specific, MultiLingual, and Cross-Lingual Text Summarization: A Wide-Ranging Overview Luca Cagliero Politecnico di Torino, Italy