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.

Author identification based on functional summarization

Abstract: Author identification based on functional summarization is disclosed. One example is a system including a plurality of summarization engines, each summarization engine to receive, via a processing system, a text content to provide a summary of the text content. At least one meta-aigorithmic pattern is applied to at least two summaries to provide a meta-summary of the text content using the at least two summaries. A content processor identifies, from the meta-summary, authorship features associated with the text content. An evaluator determines, for a given category, a category value of the meta- summary, the category value indicative of a similarity of the authorship features to the category. A selector selects, for the given category, a combination of meta-algorithmic patterns and summarization engines that provides the meta-summary that optimizes the category value for the text content.

Interactive phrase browsing within compressed text

Abstract: Introduction. Several techniques have been described for pattern searching in compressed text [de Moura et al., 1998, Manber, 1997]. In these methods the compression regime is usually modified in some way to suit the needs of the searching process, and compression effectiveness compromised. Furthermore, the searching mechanism used is usually based upon linear-search regular expression pattern matching, meaning that it can be expensive to handle large files. Finally, the whole nature of the exact-match searching process means that a small misconception in the construction of the pattern to be searched can result in a belief that the information sought is not present when in fact it is. Nevill-Manning et al. [1997] describe PHIND, a phrase-browsing approach to searching, in which a user “explores” the collection, and narrows down their region of interest step by step until their information need is met. In this presentation we build upon this approach, and show how the RE-PAIR compression regime [Larsson and Moffat, 2000] naturally supports a similar browsing capability, and also is such that locations in the text at which target phrases appear can be quickly identified, decompressed, and returned to the user. And because by-products of the compression process are used by the browser, no explicit index is required.

Multi-document summarization using concept chain graphs

Abstract: Contents List of Figures iii List of Tables iv Acknowledgements vi Abstract vii 1 Introduction 1

Towards content-level coherence with aspect-guided summarization

Abstract: The TAC 2010 summarization track initiated a new task—aspect-guided summarization—that centers on textual aspects embodied as particular kinds of information of a text. We observe that aspect-guided summaries not only address highly specific user need, but also facilitate content-level coherence by using aspect information. In this article, we present a full-fledged approach to aspect-guided summarization with a focus on summary coherence. Our summarization approach depends on two prerequisite subtasks: recognizing aspect-bearing sentences in order to do sentence extraction, and modeling aspect-based coherence with an HMM model in order to predict a coherent sentence ordering. Using the manually annotated TAC 2010 and 2010 datasets, we validated the effectiveness of our proposed methods for those subtasks. Drawing on the empirical results, we proceed to develop an aspect-guided summarizer based on a simple but robust base summarizer. With sentence selection guided by aspect information, our system is one of the best on TAC 2011. With sentence ordering predicted by the aspect-based HMM model, the summaries achieve good coherence.