Topic
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.
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TL;DR: The main findings are presented while integrating the research results of experiments on legal document summarization by other research groups, and novel avenues of research for automatic text summarization are proposed.
Abstract: In the field of law there is an absolute need for summarizing the texts of court decisions in order to make the content of the cases easily accessible for legal professionals. During the SALOMON and MOSAIC projects we investigated the summarization and retrieval of legal cases. This article presents some of the main findings while integrating the research results of experiments on legal document summarization by other research groups. In addition, we propose novel avenues of research for automatic text summarization, which we currently exploit when summarizing court decisions in the ACILA project. Techniques for automated concept learning and argument recognition are here the most challenging.
40 citations
Book•
31 Oct 2011TL;DR: This chapter sets the focus on automatic summarization of text using as few direct human resources as possible, resulting in what can be perceived as an intermediary system, and presents the notion of taking a holistic view of the generation of summaries.
Abstract: Today, with digitally stored information available in abundance, even for many less commonly spoken languages, this information must by some means be filtered and extracted in order to avoid drowning in it. Automatic summarization is one such technique, where a computer summarizes a longer text into a shorter non-redundant form. The development of advanced summarization systems also for smaller languages may unfortunately prove too costly. Nevertheless, there will still be a need for summarization tools for these languages in order to curb the immense flow of digital information. This chapter sets the focus on automatic summarization of text using as few direct human resources as possible, resulting in what can be perceived as an intermediary system. Furthermore, it presents the notion of taking a holistic view of the generation of summaries.
40 citations
TL;DR: This paper proposes a novel approach developed based on the spectral analysis to simultaneously clustering and ranking of sentences and demonstrates the improvement of the proposed approach over the other existing clustering-based approaches.
40 citations
Proceedings Article•
25 Jul 2015TL;DR: The authors proposed a reader-aware multi-document summarization (RA-MDS) approach, where a set of reader comments associated with the news reports are also collected to calculate the saliency of the text units by jointly considering news reports and reader comments.
Abstract: We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS). Specifically, a set of reader comments associated with the news reports are also collected. The generated summaries from the reports for the event should be salient according to not only the reports but also the reader comments. To tackle this RAMDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments. Another reader-aware characteristic of our framework is to improve linguistic quality via entity rewriting. The rewriting consideration is jointly assessed together with other summarization requirements under a unified optimization model. To support the generation of compressive summaries via optimization, we explore a finer syntactic unit, namely, noun/verb phrase. In this work, we also generate a data set for conducting RA-MDS. Extensive experiments on this data set and some classical data sets demonstrate the effectiveness of our proposed approach.
40 citations
TL;DR: This work explores a novel use of recurrent neural network language modeling (RNNLM) framework for extractive broadcast news summarization, and demonstrates the performance merits of the summarization methods when compared to several well-studied state-of-the-art unsupervised methods.
Abstract: Extractive text or speech summarization manages to select a set of salient sentences from an original document and concatenate them to form a summary, enabling users to better browse through and understand the content of the document. A recent stream of research on extractive summarization is to employ the language modeling (LM) approach for important sentence selection, which has proven to be effective for performing speech summarization in an unsupervised fashion. However, one of the major challenges facing the LM approach is how to formulate the sentence models and accurately estimate their parameters for each sentence in the document to be summarized. In view of this, our work in this paper explores a novel use of recurrent neural network language modeling (RNNLM) framework for extractive broadcast news summarization. On top of such a framework, the deduced sentence models are able to render not only word usage cues but also long-span structural information of word co-occurrence relationships within broadcast news documents, getting around the need for the strict bag-of-words assumption. Furthermore, different model complexities and combinations are extensively analyzed and compared. Experimental results demonstrate the performance merits of our summarization methods when compared to several well-studied state-of-the-art unsupervised methods.
40 citations