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.

Personalized Summarization of Broadcasted Soccer Videos with Adaptive Fast-Forwarding

Abstract: We propose a hybrid personalized summarization framework that combines adaptive fast-forwarding and content truncation to generate comfortable and compact video summaries. We formulate video summarization as a discrete optimization problem, where the optimal summary is determined by adopting Lagrangian relaxation and convex-hull approximation to solve a resource allocation problem. Subjective experiments are performed to demonstrate the relevance and efficiency of the proposed method.

Statistical Single-Document Summarization for Chinese News Articles

Abstract: Given huge amount of daily news articles, it would be helpful to users if the news reading time can be reduced. In this paper, we focus on single-document summarization for Chinese news articles with statistical methods. First, new vocabularies are collected from news articles, and verified with online translation services. These are included as the auxiliary lexicon. Then, statistical word segmentation is done by calculating the relative frequency of overlapping word n-grams. Finally, the sentence importance is estimated as the weighted sum of n-gram scores, and the top-ranked sentences are selected as the summary. The experimental results showed that generated summaries can be effectively clustered in the same group as the original news articles. A great reduction in storage size can be observed while preserving suitable similarity with the original document. This shows the potential of our proposed approach in news summarization. Further investigation is needed to verify in other document domains.

Text summarization based on itemized sentences and similar parts detection between documents

Abstract: In this paper, we propose a text summarizationsystem for a single document and multiple documents The system for a single one extracts sentences from a document and itemizes them to generate a summary We applied this mechanism for Task A (single document summarization) We also utilized this mechanism for multi-document summarization (Task B) except for itemization mechanism The system for multidocument detects similar parts between all summarized documents and eliminates them In the Formal Run evaluation of TSC2, our system got better evaluation for a single document summarization but multidocument summarizationwas not so good

Research and implementation of knowledge based text summarization systems

Abstract: A model of knowledge based text summarization systems is presented in this paper. With this model, a system called LADIES is designed and implemented. An evaluation approach to automatic abstracting systems is also discussed.

A sentence selection method of query-based Chinese Multi-document summarization

Abstract: With the Internet developing, there are many documents on the same topic which contains redundance. Multi-Document summarization is a technology of natural languages processing, which extract important information from multiple texts about the same topic according to the compression ratio. Sentence selection is an important part of Multi-document summarization .In this paper, we design a calculate method for Chinese words semantic similarity based on Hownet and Tongyici Cilin and we also design a calculate method for sentences semantic similarity based on N-gram. Using these methods, we evaluate the importance of each candidate sentence based on exploiting both the feature of correlation with the query and the feature of the global association feature. We use an improved MMR method to select the sentence in order to reducing the redundancy. The evaluation and results are presented, which prove that the proposed methods are efficient and the summaries generated are good.