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.

Design and Implement Hierarchical Topic Knowledge Map for Chinese News with Multi-Document Summarization

Abstract: With the development of Internet starting diverse learning chances, but that also caused gradually about the problem of information overload for users. As this study observed that on-line news portals only provide simple topic category mechanism rather than on necessary of afterward development of the event or creation of knowledge. This study utilizes text mining technique to analyze Chinese news collections and to construct Hierarchical Topic Knowledge Map (HTKM) so as to resolve the problem of information representation. According to the evaluated results, most of readers considered the representation of knowledge visualization and HTKM make high convenient on readers with understanding the context of news effectively.

WHISK: Web Hosted Information into Summarized Knowledge

Abstract: WHISK: Web Hosted Information into Summarized Knowledge

Exploring strategies for developing link analysis based question-oriented multi-document summarization models

Abstract: Graph ranking algorithms have been successfully used in multi-document summarization. Among them, the basic link analysis model has drawn much attention due to its' mutual reinforcement principle which appears to be sound for the generic summarization task. In this paper, we explore effective strategies for extending the basic link analysis model to question-oriented multi-document summarization. Three kinds of strategies, namely link re-weighting, baseset downsizing and projection, are proposed to introduce question-dependent similarity metric, adjust the node number and refine the ranking process respectively. Experimental results evaluated on the DUC data sets demonstrate that these three strategies can achieve better results.

Query-oriented Unsupervised Multi-document Summarization on Big Data

Abstract: Real time document summarization is a critical need nowadays, owing to the large volume of information available for our reading, and our inability to deal with this entirely due to limitations of time and resources. Oftentimes, information is available in multiple sources, offering multiple contexts and viewpoints on a single topic of interest. Automated multi-document summarization (MDS) techniques aim to address this problem. However, current techniques for automated MDS suffer from low precision and accuracy with reference to a given subject matter, when compared to those summaries prepared by humans and takes large time to create the summary when the input given is too huge. In this paper, we propose a hybrid MDS technique combining feature based algorithms and dynamic programming for generating a summary from multiple documents based on user provided query. Further, in real-world scenarios, Web search serves up a large number of URLs to users, and the work of making sense of these with reference to a particular query is left to the user. In this context, an efficient parallelized MDS technique based on Hadoop is also presented, for serving a concise summary of multiple Webpage contents for a given user query in reduced time duration.