BACKGROUND PubMed data potentially can provide decision support information, but PubMed was not exclusively designed to be a point-of-care tool. Natural language processing applications that summarize PubMed citations hold promise for extracting decision support information. The objective of this study was to evaluate the efficiency of a text summarization application called Semantic MEDLINE, e...

We model document summarization as a nonlinear 0-1 programming problem where an objective function is defined as Heronian mean of the objective functions enforcing the coverage and diversity. The proposed model implemented on a multi-document summarization task. Experiments on DUC2001 and DUC2002 datasets showed that the proposed model outperforms the other summarization methods. Index Terms – ...

Abstract. The increase of information available in the form of text, led to the need of extensive research in the area of text summarization. Early the researches in this area started with single document summarization and drove towards multi document summarization. We present here a comparative review of the recent progress in the field of multi document summarization. The strengths and weakne...

In this paper, we propose a text summarization system for a single document and multiple documents. The system for a single one extracts sentences from a document and itemizes them to generate a summary. We applied this mechanism for Task A (single document summarization). We also utilized this mechanism for multi-document summarization (Task B) except for itemization mechanism. The system for ...

We describe our analysis and modeling of the summarization process of Japanese broadcast news. We have studied the entire manual summarization process of the Japan Broadcasting Corporation (NHK). The staff of NHK has been making manual summarizations of news text on a daily basis since December 2000. We interviewed these professional abstractors and obtained a considerable amount of news summar...

Text summarization is a process to produce an abstract or a summary by selecting significant portion of the information from one or more texts. In an automatic text summarization process, a text is given to the computer and the computer returns a shorter less redundant extract or abstract of the original text(s). Many techniques have been developed for summarizing English text(s). But, a very f...

In TAC 2008 we participated in the main task (Update Summarization) as well as the Sentiment Summarization pilot task. We modified the FastSum system (Schilder and Kondadadi, 2008) and added more aggressive filtering in order to adapt the system to update summarization and sentiment summarization. For the Update Summarization task, we show that a classifier that identifies sentences that are si...

In this paper we, present (1) an unsupervised system for AESOP task and (2) a generic multi-document summarization system for guided summarization task. We propose the use of: (1) the role and importance of words and sentences in document and, (2) number and coverage strength of topics in document for both AESOP and Guided summarization task. We also use some other statistical features, simple ...

We carry out a comprehensive study of acoustic/prosodic, linguistic and structural features for speech summarization, contrasting two genres of speech, namely Broadcast News and Lecture Speech. We find that acoustic and structural features are more important for Broadcast News summarization due to the speaking styles of anchors and reporters, as well as typical news story flow. Due to the relat...

Automatic summarization has started to receive increasing attention in recent years due to the increased amount of information available in electronic form. Especially, summarization techniques can be very useful in improving the effectiveness of information retrieval on the World Wide Web. However, currently available major search engines such as Google show only a limited capability for summa...