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.

An automatic event-complementing human life summarization scheme based on a social computing method over social media content

Abstract: This paper proposes a human life summarization scheme based on multimedia content published on social media. In this context the term "life" includes the events, occasions and activities users post on their walls. Towards this direction, an innovative architecture is designed that consists of two modules: the content preparation and the content summarization module. During content preparation, a Social Media web page is automatically segmented into tokens. Next multimedia content is kept and it is associated to its respective metadata (date of post, events, likes, persons, comments etc.) after filtering information through the YAGO2 knowledge base. Then a novel ranking mechanism puts multimedia content in order of importance based on a social computing methodology. Finally the summarization module produces a meaningful video clip that includes the top moments of one's life without completely disregarding the less important. To the best of the authors' knowledge, this is one of the first human life summarization schemes that are based on social media content. Experimental results illustrate the promising performance of the proposed architecture and set a basis for future research.

A Hybrid Method for Query based Automatic Summarization System

Abstract: Automatic text summarization is one of the research goals of Natural Language Processing which relieves humans from studying each and every line in a text document to understand the underlying concepts in it. Automatic text summarization is aimed to create a brief outline of a given text covering the important points in the text. Automatic text summarization can be generic or query specific. This paper is focused on Query specific text summarization where a summary of the given text is constructed based on the given query. Query specific text summarization is based on the calculation of the relationship between sentences in the text document and the query given. Several statistical techniques and linguistic techniques have been developed to find the relationship between the given query and the sentences in the document. These methods when used alone could not give desired accuracy in the results. In this paper a sentence scoring method is defined based on existing sentence scoring methods. It attempts to combine the individual results of these methods to give a better assessment of the relationship between the sentences.

Solving Multi-Document Summarization as an Orienteering Problem

Abstract: With advances in information technology, people face the problem of dealing with tremendous amounts of information and need ways to save time and effort by summarizing the most important and relevant information. Thus, automatic text summarization has become necessary to reduce the information overload. This article proposes a novel extractive graph-based approach to solve the multi-document summarization (MDS) problem. To optimize the coverage of information in the output summary, the problem is formulated as an orienteering problem and heuristically solved by an ant colony system algorithm. The performance of the implemented system (MDS-OP) was evaluated on DUC 2004 (Task 2) and MultiLing 2015 (MMS task) benchmark corpora using several ROUGE metrics, as well as other methods. Its comparison with the performances of 26 systems shows that MDS-OP achieved the best F-measure scores on both tasks in terms of ROUGE-1 and ROUGE-L (DUC 2004), ROUGE-SU4, and three other evaluation methods (MultiLing 2015). Overall, MDS-OP ranked among the best 3 systems.

Efficient summarization-aware search for online news articles

Abstract: News portals gather and organize news articles published daily on the Internet. Typically, news articles are clustered into 'events' and each cluster is displayed with a short description of its contents. A particularly interesting choice for describing the contents of a cluster is a machine-generated multi-document summary of the articles in the cluster. Such summaries are informative and help news readers to identify and explore only clusters of interest. Naturally, multi-document clusters and summaries are also valuable to help users navigate the results of keyword-search queries. Unfortunately, current document summarizers are still slow; as a result, search strategies that define document clusters and their multi-document summaries online, in a query-specific manner, are prohibitively expensive. In contrast, search strategies that only return offline, query-independent document clusters are efficient, but might return clusters whose (query-independent) summaries are of little relevance to the queries. In this paper, we present an efficient Hybrid search strategy to address the limitations of fully online and fully offline summarization-aware search approaches. Extensive experiments involving user relevance judgments and real news articles show that the quality of our Hybrid results is high, and that these results are computed in substantially less time than with the fully online strategy. We have implemented our strategy and made it available on the Newsblaster news summarization system, which crawls and summarizes news articles from a variety of web sources on a daily basis.

Using summarization for automatic briefing generation

Abstract: We describe a system which automatically generates multimedia briefings from high-level outlines. The system uses summarization in content selection and creation, and in helping form a coherent narrative for the briefing. The approach does not require a domain knowledge base.