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...

We study a new content-based method for the evaluation of text summarization systems without human models which is used to produce system rankings. The research is carried out using a new content-based evaluation framework called FRESA to compute a variety of divergences among probability distributions. We apply our comparison framework to various well-established content-based evaluation measu...

In this era, where electronic text information is exponentially growing and where time is a critical resource, it has become virtually impossible for any user to browse or read large numbers of individual documents. It is therefore important to explore methods of allowing users to locate and browse information quickly within collections of documents. Automatic text summarization of multiple doc...

Rapid improvement of electronic documents in World Wide Web has made overload to the users in accessing the information. Therefore, abstracting the primary content from numerous documents related to same topic is highly essential. Summarization of multiple documents helps in valuable decision-making in less time. This paper proposed a framework named Adept Multi-Document Summarization (AMDS) fo...

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...

In this paper, we describe the following two approaches to summarization: (1) only sentence extraction, (2) sentence extraction + bunsetsu elimination. For both approaches, we use the machine learning algorithm called Support Vector Machines. We participated in both Task-A (single-document summarization task) and Task-B (multi-document summarization task) of TSC-2.

Single-document summarization aims to reduce the size of a text document while preserving the most important information. Much work has been done on open-domain summarization. This paper presents an automatic way to mine domain-specific patterns from text documents. With a small amount of effort required for manual selection, these patterns can be used for domain-specific scenario-based documen...

A New Approach to Automatic Summarization by Using Latent Dirichlet Allocation in Conditional Random Field Xiaofeng Wu, Chengqing Zong (National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing 100190, China) Abustract: In recent years, Latent Dirichlet Allocation(LDA) has been used more and more in Document Clustering, Classification, Segmentation, and some one has used it in ...