Graph summarization is the problem of producing smaller graph representations an input dataset, in such a way that compressed graphs capture relevant structural information for downstream tasks. One recent methods formulates optimal transport-based framework allows prior about node, edge, and a...

This paper presents a pilot study of opinion summarization on conversations. We create a corpus containing extractive and abstractive summaries of speaker’s opinion towards a given topic using 88 telephone conversations. We adopt two methods to perform extractive summarization. The first one is a sentence-ranking method that linearly combines scores measured from different aspects including top...

Information of interest to users is often distributed over a set of documents. Users can specify their request for information as a query/topic – a set of one or more sentences or questions. Producing a good summary of the relevant information relies on understanding the query and linking it with the associated set of documents. To “understand” the query we expand it using encyclopedic knowledg...

We propose a unified approach for summarization based on the analysis of video structures and video highlights. Our approach emphasizes both the content balance and perceptual quality of a summary. Normalized cut algorithm is employed to globally and optimally partition a video into clusters. A motion attention model based on human perception is employed to compute the perceptual quality of sho...

RDF Graph Summarization pertains to the process of extracting concise but meaningful summaries from RDF Knowledge Bases (KBs) representing as close as possible the actual contents of the KB both in terms of structure and data. RDF Summarization allows for better exploration and visualization of the underlying RDF graphs, optimization of queries or query evaluation in multiple steps, better unde...

We present a method for summarizing document by creating a semantic graph of the original document and identifying the substructure of such a graph that can be used to extract sentences for a document summary. We start with deep syntactic analysis of the text and, for each sentence, extract logical form triples, subject–predicate–object. We then apply cross-sentence pronoun resolution, co-refer...

Update summarization is to summarize a document collection B given that the users have already read another document collection A, which has time stamp prior to that of B. An important and challenging issue in update summarization is that contents in B already covered by A should be excluded from the update summary. In this paper, we propose a graphbased regularization framework MarginRank for ...

ive Meeting Summarization Using Dependency Graph Fusion Siddhartha Banerjee The Pennsylvania State University University Park PA, USA 16802 [email protected] Prasenjit Mitra Qatar Computing Research Institute Tornado Tower, 18th floor Doha, Qatar [email protected] Kazunari Sugiyama National University of Singapore 13 Computing Drive Singapore 117417 [email protected]

In this paper we explore the problem of document summarization in Persian language from two distinct angles. In our first approach, we modify a popular and widely cited Persian document summarization framework to see how it works on a realistic corpus of news articles. Human evaluation on generated summaries shows that graph-based methods perform better than the modified systems. We carry this ...

Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we aim at developing an abstractive summarizer. First, our proposed approach identifies the most important document in the multi-document set. The sentences in the most important document are aligned to sentences in other ...