Multiple document summarization is becoming increasingly important as a way of reducing information overload, particularly in the context of the proliferation of similar accounts of events that are available on the Web. Removal of similar sentences often results in either partial or unwanted elimination of important information. In this paper, we present an approach to split sentences into thei...

Multi-document summarization is a fundamental tool for document understanding and has received much attention recently. Given a collection of documents, a variety of summarization methods based on different strategies have been proposed to extract the most important sentences from the original documents. However, very few studies have been reported on aggregating different summarization methods...

We present two approaches to email thread summarization: Collective Message Summarization (CMS) applies a multi-document summarization approach, while Individual Message Summarization (IMS) treats the problem as a sequence of single-document summarization tasks. Both approaches are implemented in our general framework driven by sentence compression. Instead of a purely extractive approach, we e...

With the rapid development of modern technology electronically available textual information has increased to a considerable amount. Summarization of textual information manually from unstructured text sources creates overhead to the user, therefore a systematic approach is required. Summarization is an approach that focuses on providing the user with a condensed version of the original text bu...

Since 2001, the Document Understanding Conferences have been the forum for researchers in automatic text summarization to compare methods and results on common test sets. Over the years, several types of summarization tasks have been addressed—single document summarization, multi-document summarization, summarization focused by question, and headline generation. This paper is an overview of the...