Multi-document summarization aims to distill the most important information from a set of documents to generate a compressed summary. Given a sentence graph generated from a set o...
This paper describes a novel approach of improving multi-document summarization based on cross-document information extraction (IE). We describe a method to automatically incorpora...
Update summarization aims to create a summary over a topic-related multi-document dataset based on the assumption that the user has already read a set of earlier documents of the ...
In this paper we investigate a novel and important problem in multi-document summarization, i.e., how to extract an easy-tounderstand English summary for non-native readers. Exist...
In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific informat...
Lucy Vanderwende, Hisami Suzuki, Chris Brockett, A...
This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization—a “parse-and-trim” approach and a...
David M. Zajic, Bonnie J. Dorr, Jimmy J. Lin, Rich...
Multi-document summarization aims to create a compressed summary while retaining the main characteristics of the original set of documents. Many approaches use statistics and mach...
Dingding Wang, Tao Li, Shenghuo Zhu, Chris H. Q. D...
Extraction based Multi-Document Summarization Algorithms consist of choosing sentences from the documents using some weighting mechanism and combining them into a summary. In this...
We show that a simple procedure based on maximizing the number of informative content-words can produce some of the best reported results for multi-document summarization. We fir...
The increasing complexity of summarization systems makes it difficult to analyze exactly which modules make a difference in performance. We carried out a principled comparison be...