Abstract We have introduced information extraction technique such as named entity tagging and pattern discovery to a summarization system based on sentence extraction technique, and evaluated the performance in the Document Understanding Conference 2001 (DUC-2001). We participated in the Single Document Summarization task in DUC-2001 and achieved one of the best performance in subjective evalua...

Text summarization endeavors to produce a summary version of a text, while maintaining the original ideas. The textual content on the web, in particular, is growing at an exponential rate. The ability to decipher through such massive amount of data, in order to extract the useful information, is a major undertaking and requires an automatic mechanism to aid with the extant repository of informa...

today, with rapid growth of the world wide web and creation of internet sites and online text resources, text summarization issue is highly attended by various researchers. extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. when, we are facing into large data volume documents, the extr...

Sentence extraction is a valuable technique for automatic summarization. This paper presents LIC2M’s first participation in TAC evaluation campaign (update summarization task). We describe two main extractive approaches for summarization. The semantic strategy makes use of a bag-of-senses to calculate sense concentration on each sentence. In the lexical strategy, source text sentences are ranke...

Evaluauon is a key part of any research and development effort, but the goals and focus of evaluat:ons are often narrow m scope, addressing a specific algonthm or technique, or analyzing a single result All of the evaluation work clone to date on text summarization systems has been by the developers of mdlvldual systems, usually to study and improve sentence selection cntena Under TIPSTER III, ...

We participated in two summarization tasks at the TSC-3. We have introduced categorization of feature values for our summarization system, which is based on sentence extraction technique. The categorized values were used as features for generating a decision tree. We compared our summarization system using the categorization of feature values with the one using linear combination of features in...

Today, with rapid growth of the World Wide Web and creation of Internet sites and online text resources, text summarization issue is highly attended by various researchers. Extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. When, we are facing into large data volume documents, the extr...

Ontology summarization has been recognized as a very useful technique to facilitate ontology understanding and then support ontology reuse as a new or supplementing technique. A number of efforts have emerged lately that apply different criteria, addressing different features of ontology, to extract ontology summaries. However, those efforts are ad-hoc in that there lacks consensus on a number ...

In this paper, we discuss techniques, algorithms, evaluation methods used in online, offline, supervised, unsupervised, multi-video and clustering for Video Summarization/Multi-view Summarization from various references. We have studied different techniques the literature described features generating video summaries with methods, algorithms datasets used. covered survey towards new frontier of...

This paper deals with using latent semantic analysis in text summarization. We describe a generic text summarization method which uses the latent semantic analysis technique to identify semantically important sentences. This method has been further improved. Then we propose two new evaluation methods based on LSA, which measure content similarity between an original document and its summary. In...