The technologies for singleand multi-document summarization that are described and evaluated in this article can be used on heterogeneous texts for different summarization tasks. They refer to the extraction of important sentences from the documents, compressing the sentences to their essential or relevant content, and detecting redundant content across sentences. The technologies are tested at...

Title of dissertation: Text Summarization Evaluation: Correlating Human Performance on an Extrinsic Task with Automatic Intrinsic Metrics Stacy F. Hobson Doctor of Philosophy, 2007 Dissertation directed by: Professor Bonnie J. Dorr Department of Computer Science Text summarization evaluation is the process of assessing the quality of an individual summary produced by human or automatic methods....

Video summarization algorithms present condensed versions of a full length video by identifying the most significant parts of the video. In this paper, we propose an automatic video summarization method using the subtitles of videos and text summarization techniques. We identify significant sentences in the subtitles of a video by using text summarization techniques and then we compose a video ...

Automatic summarization is an important task as a form of human support technology. We propose in this paper a new summarization method that is based on example-based approach. Using example-based approach for the summarization task has the following three advantages: high modularity, absence of the necessity to score importance for each word, and high applicability to local context. Experiment...

In this paper we investigate a new task of automatically constructing an overview article from a given set of news articles about a news event. We propose a news synthesis approach to address this task based on passage segmentation, ranking, selection and merging. Our proposed approach is compared with several typical multi-document summarization methods on the Wikinews dataset, and achieves th...

Huge volumes of textual information has been produced every single day. In order to organize and understand such large datasets, in recent years, summarization techniques have become popular. These techniques aims at finding relevant, concise and non-redundant content from such a big data. While network methods have been adopted to model texts in some scenarios, a systematic evaluation of multi...

This paper presents an approach to summarize single scientific papers, by extracting its contributions from the set of citation sentences written in other papers. Our methodology is based on extracting significant keyphrases from the set of citation sentences and using these keyphrases to build the summary. Comparisons show how this methodology excels at the task of single paper summarization, ...

In this paper we present a generic, language independent multi-document summarization system forming extracts using the cover coefficient concept. Cover Coefficient-based Summarizer (CCS) uses similarity between sentences to determine representative sentences. Experiments indicate that CCS is an efficient algorithm that is able to generate quality summaries online.

We have proposed an automatic speech summarization approach that extracts words from transcription results obtained by automatic speech recognition (ASR) systems. To numerically evaluate this approach, the automatic summarization results are compared with manual summarization generated by humans through word extraction. We have proposed three metrics, weighted word precision, word strings preci...

We have proposed an automatic speech summarization approach that extracts words from transcription results obtained by automatic speech recognition (ASR) systems. To numerically evaluate this approach, the automatic summarization results are compared with manual summarization generated by human subjects through word extraction. We have proposed three metrics, weighted word precision, word strin...