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Abstract

Summarization entails analysis of source material, selection of key information, condensation of this, and generation of a compct summary form. While there habe been many investigations into the automatic summarization of text, relatively little attention has been given to the summarization of information from structured information sources such as data of knowledge bases, despite this being a desirable capability for a number of application areas including report generation from databases (e.g. weather, financial, medical) and simulation (e.g. military, manufacturing, aconomic). After a brief introduction indicating the main elements of summarization and referring to some illustrative approaches to it, considers pecific issues in the generation of text summaries of event data, describes a system, SumGen, which selects key information from an event database by reasoning about event frequencies, frequencies of relations between events, and domain specific importance measures. Describes how Sum Gen then aggregates similar information and plans a summary presentations tailored to stereotypical users

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Abstract

The high quality evaluation of generated summaries is needed if we are to improve automatic summarization systems. Although human evaluation provides better results than automatic evaluation methods, its cost is huge and it is difficult to reproduce the results. Therefore, we need an automatic method that simulates human evaluation if we are to improve our summarization system efficiently. Although automatic evaluation methods have been proposed, they are unreliable when used for individual summaries. To solve this problem, we propose a supervised automatic evaluation method based on a new regression model called the voted regression model (VRM). VRM has two characteristics: (1) model selection based on 'corrected AIC' to avoid multicollinearity, (2) voting by the selected models to alleviate the problem of overfitting. Evaluation results obtained for TSC3 and DUC2004 show that our method achieved error reductions of about 17-51% compared with conventional automatic evaluation methods. Moreover, our method obtained the highest correlation coefficients in several different experiments.

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Abstract

Summary writing is a process for creating a short version of a source text. It can be used as a measure of understanding. As grading students' summaries is a very time-consuming task, computer-assisted assessment can help teachers perform the grading more effectively. Several techniques, such as BLEU, ROUGE, N-gram co-occurrence, Latent Semantic Analysis (LSA), LSA_Ngram and LSA_ERB, have been proposed to support the automatic assessment of students' summaries. Since these techniques are more suitable for long texts, their performance is not satisfactory for the evaluation of short summaries. This paper proposes a specialized method that works well in assessing short summaries. Our proposed method integrates the semantic relations between words, and their syntactic composition. As a result, the proposed method is able to obtain high accuracy and improve the performance compared with the current techniques. Experiments have displayed that it is to be preferred over the existing techniques. A summary evaluation system based on the proposed method has also been developed.

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Abstract

Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video. Movie summarization condenses a full-length movie into a summary that still retains the most significant and interesting content of the original movie. In the past, several movie summarization systems have been proposed to generate a movie summary based on low-level video features such as color, motion, texture, etc. However, a generic summary, which is common to everyone and is produced based only on low-level video features will not satisfy every user. As users' preferences for the summary differ vastly for the same movie, there is a need for a personalized movie summarization system nowadays. To address this demand, this paper proposes a novel system to generate semantically meaningful video summaries for the same movie, which are tailored to the preferences and interests of a user. For a given movie, shots and scenes are automatically detected and their high-level features are semi-automatically annotated. Preferences over high-level movie features are explicitly collected from the user using a query interface. The user preferences are generated by means of a stored-query. Movie summaries are generated at shot level and scene level, where shots or scenes are selected for summary skim based on the similarity measured between shots and scenes, and the user's preferences. The proposed movie summarization system is evaluated subjectively using a sample of 20 subjects with eight movies in the English language. The quality of the generated summaries is assessed by informativeness, enjoyability, relevance, and acceptance metrics and Quality of Perception measures. Further, the usability of the proposed summarization system is subjectively evaluated by conducting a questionnaire survey. The experimental results on the performance of the proposed movie summarization approach show the potential of the proposed system.

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Abstract

Describes the design and implementation of a text extraction tool, NEWS_EXT, which aztomatically produces summaries from news reports by extracting sentences to form indicative abstracts. Selection of sentences is based on sentence importance, measured by means of sentence scoring or simple linguistic analysis of sentence structure. Tests were conducted on 4 approaches for the functioning of the NEWS_EXT system; extraction by keyword frequency; extraction by title keywords; extraction by location; and extraction by indicative phrase. Reports results of a study to compare the results of the application of NEWS_EXT with manually produced extracts; using relevance as the criterion for effectiveness. 48 newspaper articles were assessed (The Straits Times, International Herald Tribune, Asian Wall Street Journal, and Financial Times). The evaluation was conducted in 2 stages: stage 1 involving abstracts produced manually by 2 human experts; stage 2 involving the generation of abstracts using NEWS_EXT. Results of each of the 4 approaches were compared with the human produced abstracts, where the title and location approaches were found to give the best results for both local and foreign news. Reports plans to refine and enhance NEWS_EXT and incorporate it as a module within a larger newspaper clipping system

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Abstract

Automatic document summarization using citations is based on summarizing what others explicitly say about the document, by extracting a summary from text around the citations (citances). While this technique works quite well for summarizing the impact of scientific articles, other genres of documents as well as other types of summaries require different approaches. In this paper, we introduce a new family of methods that we developed for legal documents summarization to generate catchphrases for legal cases (where catchphrases are a form of legal summary). Our methods use both incoming and outgoing citations, and we show how citances can be combined with other elements of cited and citing documents, including the full text of the target document, and catchphrases of cited and citing cases. On a legal summarization corpus, our methods outperform competitive baselines. The combination of full text sentences and catchphrases from cited and citing cases is particularly successful. We also apply and evaluate the methods on scientific paper summarization, where they perform at the level of state-of-the-art techniques. Our family of citation-based summarization methods is powerful and flexible enough to target successfully a range of different domains and summarization tasks.

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Footnote

Beitrag in einem 'Special issue on neuro-information science'.