Tracking and summarizing news on a daily basis with Columbia's Newsblaster

McKeown, Kathleen; Barzilay, Regina; Evans, David K.; Hatzivassiloglou, Vasileios; Klavans, Judith L.; Nenkova, Ani; Sable, Carl; Schiffman, Barry; Sigelman, Sergey

doi:10.3115/1289189.1289212

Cited by 145 publications

(105 citation statements)

References 5 publications

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“…Newsblaster (McKeown et al, 2002), was developed to summarize online news articles. The summarizer uses MultiGen McKeown et al, 1999), which identifies common sentences from news article using machine learning together with statistical techniques .…”

Section: News Summarizationmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

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Section: News Summarizationmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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“…News categorization is studied using document clustering [5] and document indexing [6]. Document summarization [7] for news is adopted by Yahoo and Google for their news services. Recently, sentiment analysis [8] is often used for analysis of social networks.…”

Section: Related Workmentioning

confidence: 99%

Personalized news recommendation using classified keywords to capture user preference

Lee

Lim

et al. 2014

16th International Conference on Advanced Communication Technology

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Abstract-Recommender systems are becoming an essential part of smart services. When building a news recommender system, we should consider special features different from other recommender systems. Hot news topics are changing every moment, thus it is important to recommend right news at the right time. This paper aims to propose a new model, based on deep neural network, to analyse user preference for news recommender system. The model extracts interest keywords to characterize the user preference from the set of news articles read by that particular user in the past. The model utilizes characterizing features for news recommendation, and applies those to the keyword classification for user preference. For the keyword classification, we use deep neural network for online preference analysis, because adaptive learning is necessary to track changes of hot topics sensitively. The usefulness of our model is validated through experiments. In addition, the accuracy and diversity of the recommendation results is also analysed.

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“…Additionally, TDT or other automatic news mining applications like for example Google News [5] concentrate more on tracking and detecting particular events than on generating their topical summaries. The part of research, which centers on temporal summarization of news articles is represented by: [1], [9], [11], [14]. In [1] novelty and usefulness measures are applied for sentences extracted from newswire resources in order to generate temporal summaries of news topics.…”

Section: Related Workmentioning

confidence: 99%

Summarization of Dynamic Content in Web Collections

Jatowt

Ishizuka

2004

Lecture Notes in Computer Science

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Abstract. This paper describes a new research proposal of multi-document summarization of dynamic content in web pages. Much information is lost in the Web due to the temporal character of web documents. Therefore adapting summarization techniques to the web genre is a promising task. The aim of our research is to provide methods for summarizing volatile content retrieved from collections of topically related web pages over defined time periods. The resulting summary ideally would reflect the most popular topics and concepts found in retrospective web collections. Because of the content and time diversities of web changes, it is necessary to apply different techniques than standard methods used for static documents. In this paper we propose an initial solution to this summarization problem. Our approach exploits temporal similarities between web pages by utilizing sliding window concept over dynamic parts of the collection.

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Tracking and summarizing news on a daily basis with Columbia's Newsblaster

Cited by 145 publications

References 5 publications

A Review on Automatic Text Summarization Approaches

A Review on Automatic Text Summarization Approaches

Personalized news recommendation using classified keywords to capture user preference

Summarization of Dynamic Content in Web Collections

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