This paper discusses passage extraction approaches to multidocument summarization that use available information about the document set as a whole and the relationships between the documents to build on single document summarization methodology. Multi-document summarization di ers from single in that the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries, as well as the user's goals in creating the summary. Our approach addresses these issues by using domain-independent techniques based mainly on fast, statistical processing, a metric for reducing redundancy and maximizing diversity in the selected passages, and a modular framework to allow easy parameterization for di erent genres, corpora characteristics and user requirements. We examined how humans create multi-document summaries as well as the characteristics of such summaries and use these summaries to evaluate the performance of various multidocument summarization algorithms.

/pdf/creating-and-evaluating-multi-document-sentence-extract-q5909lgcyb.pdf

Multi-document summarization

Creating and evaluating multi-document sentence extract summaries

Online social media has become one of the most important ways people communicate, while how to find valuable information from huge amounts of data becomes a key problem. We present a novel topic extraction method that employs topic value of each words and social model attributes as additional features based on the multi-document summarization. The experimental results show that the multi-document summarization with the topic and the sociality are helpful to extract topics from social media.

Multi-document summarization

Citations

Creating and evaluating multi-document sentence extract summaries

Extracting Main Content of a Topic on Online Social Network by Multi-document Summarization

References

Creating and evaluating multi-document sentence extract summaries

Extracting Main Content of a Topic on Online Social Network by Multi-document Summarization

Related Papers (5)

Multi-document Summarization by Generation

Evaluation of Multi Document Summarization Techniques

Dynamic Multi-Document Summarization Model: Dynamic Multi-Document Summarization Model

Dynamic Multi-Document Summarization Model

Multi-document summarization for e-learning