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.

Towards a Neural Network Approach to Abstractive Multi-Document Summarization

Abstract: Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS). However, due to the lack of large scale multi-document summaries, such methods can be hardly applied to multi-document summarization (MDS). In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS. We propose an approach to extend the neural abstractive model trained on large scale SDS data to the MDS task. Our approach only makes use of a small number of multi-document summaries for fine tuning. Experimental results on two benchmark DUC datasets demonstrate that our approach can outperform a variety of baseline neural models.

Towards Multi-Document Summarization of Scientific Articles:Making Interesting Comparisons with SciSumm

Abstract: We present a novel unsupervised approach to the problem of multi-document summarization of scientific articles, in which the document collection is a list of papers cited together within the same source article, otherwise known as a co-citation. At the heart of the approach is a topic based clustering of fragments extracted from each co-cited article and relevance ranking using a query generated from the context surrounding the co-cited list of papers. This analysis enables the generation of an overview of common themes from the co-cited papers that relate to the context in which the co-citation was found. We present a system called SciSumm that embodies this approach and apply it to the 2008 ACL Anthology. We evaluate this summarization system for relevant content selection using gold standard summaries prepared on principle based guidelines. Evaluation with gold standard summaries demonstrates that our system performs better in content selection than an existing summarization system (MEAD). We present a detailed summary of our findings and discuss possible directions for future research.

How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated Framework

Abstract: An effective integrated framework using both of summary and category informationThe summarization technique utilizes the category information from classificationThe classification technique utilizes the summary information from summarizationThis integrated framework achieves significant improvement Text summarization and classification are core techniques to analyze a huge amount of text data in the big data environment Moreover, as the need to read texts on smart phones, tablets and television as well as personal computers continues to grow, text summarization and classification techniques become more important and both of them do essential processes for text analysis in many applicationsTraditional text summarization and classification techniques have individually been considered as different research fields in this literature However, we find out that they can help each other as text summarization makes use of category information from text classification and text classification does summary information from text summarization Therefore, we propose an effective integrated learning framework using both of summary and category information in this paper In this framework, the feature-weighting method for text summarization utilizes a language model to combine feature distributions in each category and text, and one for text classification does the sentence importance scores estimated from the text summarizationIn the experiments, the performances of the integrated framework are better than ones of individual text summarization and classification In addition, the framework has some advantages of easy implementation and language independence because it is based on only simple statistical approaches and POS tagger

Analysis of Sentence Scoring Methods for Extractive Automatic Text Summarization

Abstract: Automatic text summarization is a major area of research in the domain of information systems. Most of the methods requires domain knowledge in order to produce a coherent and meaningful summary. In Extractive text summarization, sentences are scored on some features. A large number of feature based scoring methods have been proposed for extractive automatic text summarization by researchers. This paper reviews features for sentence scoring. The results on combinations of various features for scoring are discussed. ROUGE-N is used to evaluate generated summary with abstractive summary of DUC 2002 dataset.

Personalized Text Summarization Based on Important Terms Identification

Abstract: Automatic text summarization aims to address the information overload problem by extracting the most important information from a document, which can help a reader to decide whether it is relevant or not. In this paper we propose a method of personalized text summarization which improves the conventional automatic text summarization methods by taking into account the differences in readers' characteristics. We use annotations added by readers as one of the sources of personalization. We have experimentally evaluated the proposed method in the domain of learning, obtaining better summaries capable of extracting important concepts explained in the document when considering the relevant domain terms in the process of summarization.