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.

Structured summarization for news events

Abstract: Helping users to understand the news is an acute problem nowadays as the users are struggling to keep up with tremendous amount of information published every day in the Internet. In this research, we focus on modelling the content of news events by their semantic relations with other events, and generating structured summarization.

Open-Domain Multi-Document Summarization via Information Extraction: Challenges and Prospects

Abstract: Information Extraction (IE) and Summarization share the same goal of extracting and presenting the relevant information of a document. While IE was a primary element of early abstractive summarization systems, it’s been left out in more recent extractive systems. However, extracting facts, recognizing entities and events should provide useful information to those systems and help resolve semantic ambiguities that they cannot tackle. This paper explores novel approaches to taking advantage of cross-document IE for multi-document summarization. We propose multiple approaches to IE-based summarization and analyze their strengths and weaknesses. One of them, re-ranking the output of a high performing summarization system with IE-informed metrics, leads to improvements in both manually-evaluated content quality and readability.

Web content summarization using social bookmarks: a new approach for social summarization

Abstract: An increasing number of Web applications are allowing users to play more active roles for enriching the source content. The enriched data can be used for various applications such as text summarization, opinion mining and ontology creation. In this paper, we propose a novel Web content summarization method that creates a text summary by exploiting user feedback (comments and tags) in a social bookmarking service. We had manually analyzed user feedback in several representative social services including del.icio.us, Digg, YouTube, and Amazon.com. We found that (1) user comments in each social service have its own characteristics with respect to summarization, and (2) a tag frequency rank does not necessarily represent its usefulness for summarization. Based on these observations, we conjecture that user feedback in social bookmarking services is more suitable for summarization than other type of social services. We implemented prototype system called SSNote that analyzes tags and user comments in del.icio.us, and extracts summaries. Performance evaluations of the system were conducted by comparing its output summary with manual summaries generated by human evaluators. Experimental results show that our approach highlights the potential benefits of user feedback in social bookmarking services.

Context-informed summarization

Abstract: A method and computer program product for context-informed summarization is described. A method may comprise determining, via a computing device, a context of a communication. The method may further comprise determining, via the computing device, a summarization attribute for the communication based upon, at least in part, the context of the communication. The method may also comprise creating a summary of the communication based upon, at least in part, the summarization attribute.

Automatic summarization of music videos

Abstract: In this article, we propose a novel approach for automatic music video summarization. The proposed summarization scheme is different from the current methods used for video summarization. The music video is separated into the music track and video track. For the music track, a music summary is created by analyzing the music content using music features, an adaptive clustering algorithm, and music domain knowledge. Then, shots in the video track are detected and clustered. Finally, the music video summary is created by aligning the music summary and clustered video shots. Subjective studies by experienced users have been conducted to evaluate the quality of music summaries and effectiveness of the proposed summarization approach. Experiments are performed on different genres of music videos and comparisons are made with the summaries generated based on music track, video track, and manually. The evaluation results indicate that summaries generated using the proposed method are effective in helping realize users' expectations.