基于内容的加权共识汇总
Content based Weighted Consensus Summarization
摘要：
多文档摘要在过去的几十年里受到了广泛的关注。已经提出了几种方法，其中许多方法表现同样好，并且越来越难以选择一种特定的系统而不是另一种。能够利用每个单独系统的优势的此类系统的集合可以构建更好和更鲁棒的摘要。尽管如此，在这方面的尝试很少。在本文中，我们描述了一类集成系统，使用候选系统之间的共识，以建立一个更好的元摘要。我们强调这样的系统的两个主要缺点：无法考虑到个别系统的相对性能和忽视的内容，有利于句子排名的候选摘要。我们提出了一种替代方法，基于内容的加权共识摘要，解决这些问题。我们使用伪相关摘要来估计单个候选系统的性能，然后使用这些信息来生成更好的综合排名。DUC 2003和DUC 2004数据集上的实验表明，该系统优于现有的共识为基础的技术的大幅度。
英文摘要：
Multi-document summarization has received a great deal of attention in the past couple of decades. Several approaches have been proposed, many of which perform equally well and it is becoming in- creasingly difficult to choose one particular system over another. An ensemble of such systems that is able to leverage the strengths of each individual systems can build a better and more robust summary. Despite this, few attempts have been made in this direction. In this paper, we describe a category of ensemble systems which use consensus between the candidate systems to build a better meta-summary. We highlight two major shortcomings of such systems: the inability to take into account relative performance of individual systems and overlooking content of candidate summaries in favour of the sentence rankings. We propose an alternate method, content-based weighted consensus summarization, which address these concerns. We use pseudo-relevant summaries to estimate the performance of individual candidate systems, and then use this information to generate a better aggregate ranking. Experiments on DUC 2003 and DUC 2004 datasets show that the proposed system outperforms existing consensus-based techniques by a large margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
