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 improved approach to extract document summaries based on popularity

Abstract: With the rapid growth of the Internet, most of the textual data in the form of newspapers, magazines and journals tend to be available on-line. Summarizing these texts can aid the users access the information content at a faster pace. However, doing this task manually is expensive and time-consuming. Automatic text summarization is a solution for dealing with this problem. For a given text, a text summarization algorithm selects a few salient sentences based on certain features. In the literature, weight-based, foci-based, and machine learning approaches have been proposed. In this paper, we propose a popularity-based approach for text summarization. A popularity of the sentence is determined based on the number of other sentences similar to it. Using the notion of popularity, it is possible to extract potential sentences for summarization that could not be extracted by the existing approaches. The experimental results show that by applying both popularity and weight-based criteria it is possible to extract effective summaries.

Compact explanatory opinion summarization

Abstract: In this paper, we propose a novel opinion summarization problem called compact explanatory opinion summarization (CEOS) which aims to extract within-sentence explanatory text segments from input opinionated texts to help users better understand the detailed reasons of sentiments. We propose and study general methods for identifying candidate boundaries and scoring the explanatoriness of text segments using Hidden Markov Models. We create new data sets and use a new evaluation measure to evaluate CEOS. Experimental results show that the proposed methods are effective for generating an explanatory opinion summary, outperforming a standard text summarization method.

Spoken Language Condensation in the 21st Century

Abstract: While the field of Information Retrieval originally had the search for the most relevant documents in mind, it has become increasingly clear that in many instances, what the user wants is a piece of coherent information, derived from a set of relevant documents and possibly other sources. Reducing relevant documents, passages, and sentences to their core is the task of text summarization or information condensation. Applying text-based technologies to speech is not always workable and often not enough to capture speech specific phenomena. In this paper, we will contrast speech summarization with text summarization, give an overview of the history of speech summarization, its current state, and, finally, sketch possible avenues as well as remaining challenges in future research.