LexRank：基于图的词汇中心性显著性文本摘要
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
摘要：
介绍了一种基于随机图的文本单元相对重要性计算方法，用于自然语言处理。我们测试的问题上的文本摘要（TS）的技术。提取TS依赖于句子显著性的概念来识别文档或文档集合中最重要的句子。显著性通常根据特定重要词的存在或根据与质心伪句子的相似性来定义。我们考虑一种新的方法，LexRank，用于计算句子的重要性的概念的基础上的特征向量中心的图形表示的句子。该模型采用基于句内余弦相似度的连接矩阵作为句子图形表示的邻接矩阵。我们的系统，基于LexRank排名第一位，在一个以上的任务在最近的DUC 2004年的评估。在本文中，我们提出了一个详细的分析，我们的方法，并将其应用到一个更大的数据集，包括早期DUC评估的数据。我们讨论了几种使用相似图计算中心度的方法。结果表明，在大多数情况下，基于度的方法（包括LexRank）优于基于质心的方法和其他参与DUC的系统。此外，与阈值方法的LexRank优于其他度为基础的技术，包括连续的LexRank。我们还表明，我们的方法是相当不敏感的数据，可能会导致从一个不完美的主题聚类的文件中的噪声。
英文摘要：
We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.
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