Topic
Multi-document summarization
About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: A unified framework for extracting standard and update summaries from a set of documents using a topic modeling approach for salience determination and a dynamic modeling approach is proposed for redundancy control is presented.
Abstract: This article presents a unified framework for extracting standard and update summaries from a set of documents. In particular, a topic modeling approach is employed for salience determination and a dynamic modeling approach is proposed for redundancy control. In the topic modeling approach for salience determination, we represent various kinds of text units, such as word, sentence, document, documents, and summary, using a single vector space model via their corresponding probability distributions over the inherent topics of given documents or a related corpus. Therefore, we are able to calculate the similarity between any two text units via their topic probability distributions. In the dynamic modeling approach for redundancy control, we consider the similarity between the summary and the given documents, and the similarity between the sentence and the summary, besides the similarity between the sentence and the given documents, for standard summarization while for update summarization, we also consider the similarity between the sentence and the history documents or summary. Evaluation on TAC 2008 and 2009 in English language shows encouraging results, especially the dynamic modeling approach in removing the redundancy in the given documents. Finally, we extend the framework to Chinese multi-document summarization and experiments show the effectiveness of our framework.
11 citations
TL;DR: This work has developed a sentence extraction based multi-document summarization system using the principle of vertex cover algorithm which automatically selects relevant sentences that cover the predominant concepts of the input documents.
11 citations
11 citations
TL;DR: The proposed system performs the best in terms of precision and gets the best f-score after the summaries are preprocessed and the proposed system implements the system in three different modules: point extraction, point curation, and summary generation.
11 citations
01 Jan 2005
TL;DR: A method for extracting high-precision sentences for inclusion in a response, and a measure for predicting the completeness of a planned response are proposed for the automatic generation of email responses to helpdesk requests.
Abstract: We present a corpus-based approach for the automatic generation of email responses to helpdesk requests. This is largely an extractive multi-document summarization task. However, in our application users have a very low tolerance for responses that contain incongruous sentences. To address this problem, we propose a method for extracting high-precision sentences for inclusion in a response, and a measure for predicting the completeness of a planned response. The idea is that complete, high-precision responses may be sent to users, while incomplete responses should be passed to operators. Our results show that a small but significant proportion (14%) of our automatically generated responses have a high degree of precision and completeness, and that our measure can reliably predict the completeness of a response.
11 citations