Document Summarization Based on Data Reconstruction

Document summarization is of great value to many real world applications, such as snippets generation for search results and news headlines generation. Traditionally, document summarization is implemented by extracting sentences that cover the main topics of a document with a minimum redundancy. In this paper, we take a different perspective from data reconstruction and propose a novel framework named Document Summarization based on Data Reconstruction (DSDR). Specifically, our approach generates a summary which consist of those sentences that can best reconstruct the original document. To model the relationship among sentences, we introduce two objective functions: (1) linear reconstruction, which approximates the document by linear combinations of the selected sentences; (2) nonnegative linear reconstruction, which allows only additive, not subtractive, linear combinations. In this framework, the reconstruction error becomes a natural criterion for measuring the quality of the summary. For each objective function, we develop an efficient algorithm to solve the corresponding optimization problem. Extensive experiments on summarization benchmark data sets DUC 2006 and DUC 2007 demonstrate the effectiveness of our proposed approach. Introduction With the explosive growth of the Internet, people are overwhelmed by a large number of accessible documents. Summarization can represent the document with a short piece of text covering the main topics, and help users sift through the Internet, catch the most relevant document, and filter out redundant information. So document summarization has become one of the most important research topics in the natural language processing and information retrieval communities. In recent years, automatic summarization has been applied broadly in varied domains. For example, search engines can provide users with snippets as the previews of the document contents (Turpin et al. 2007; Huang, Liu, and Chen 2008; Cai et al. 2004; He et al. 2007). News sites usually describe hot news topics in concise headlines to facilitate browsing. Both the snippets and headlines are specific forms of document summary in practical applications. Copyright c © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Most of the existing generic summarization approaches use a ranking model to select sentences from a candidate set (Brin and Page 1998; Kleinberg 1999; Wan and Yang 2007). These methods suffer from a severe problem that top ranked sentences usually share much redundant information. Although there are some methods (Conroy and O’leary 2001; Park et al. 2007; Shen et al. 2007) trying to reduce the redundancy, selecting sentences which have both good coverage and minimum redundancy is a non-trivial task. In this paper, we propose a novel summarization method from the perspective of data reconstruction. As far as we know, our approach is the first to treat the document summarization as a data reconstruction problem. We argue that a good summary should consist of those sentences that can best reconstruct the original document. Therefore, the reconstruction error becomes a natural criterion for measuring the quality of summary. We propose a novel framework called Document Summarization based on Data Reconstruction (DSDR) which finds the summary sentences by minimizing the reconstruction error. DSDR firstly learns a reconstruction function for each candidate sentence of an input document and then obtains the error formula by that function. Finally it obtains an optimal summary by minimizing the reconstruction error. From the geometric interpretation, DSDR tends to select sentences that span the intrinsic subspace of candidate sentence space so that it is able to cover the core information of the document. To model the relationship among sentences, we discuss two kinds of reconstruction. The first one is linear reconstruction, which approximates the document by linear combinations of the selected sentences. Optimizing the corresponding objective function is achieved through a greedy method which extracts sentences sequentially. The second one is non-negative linear reconstruction, which allows only additive, not subtractive, combinations among the selected sentences. Previous studies have shown that there is psychological and physiological evidence for parts-based representation in the human brain (Palmer 1977; Wachsmuth, Oram, and Perrett 1994; Cai et al. 2011). Naturally, a document summary should consist of the parts of sentences. With the nonnegative constraints, our method leads to parts-based reconstruction so that no redundant information needs to be subtracted from the combination. We formulate the nonnegative linear reconstruction as a convex optimization problem Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence