Create Special Domain News Collections through Summarization and Classification

Abstract

In this paper, we present a novel technique to create a special domain news collection system from really simple syndication (RSS) news sites through summarization and classification. The main aim of this research is to build a self-sufficient news collection system in disaster domain. In this news collection system, we used new strategies and algorithms to mine news from RSS sites, recognized and collected disaster news using automatic summarization and classification. The most striking dissimilarity between our study and previous work is that we use a novel summary approach to improve the classification performance. This paper discusses the effect of summarization and classification model on system performance. Results show that our method yields a better performance in this field. 

visualRSS: A Platform to Mine and Visualise Social Data from RSS Feeds

“…After using a purist approach to track stories in RSS feeds focusing on public fears about science, they concluded that, despite useful information in RSS, extensive and repetitive content requires data cleansing. This pragmatic approach has been more widely adopted in recent work clustering and classifying text from RSS feeds, of which [7], [8], [9] and [10] are examples. Roesler [11] has also identified caveats here concerning the number of documents or RSS feeds/items to be clustered, semantic and linguistic issues, and the time taken to cluster content especially in a real-time application.…”

Mining and visualising information from RSS feeds: a case study

“…On the other hand, the pragmatic approach has been more widely adopted especially in classifying and clustering text from feed contents. Teng et al (2010) used automated techniques to summarise and classify RSS feeds, and applied these to items concerning disasters. Getahun et al (2009) compared the relatedness of stories to merge news items, whilst Liu et al (2009) similarly retrieved news stories from RSS feeds and classified them; therefore news items could be reorganised to allow customisable feeds by end-users.…”

Salton and Buckley’s Landmark Research in Experimental Text Information Retrieval

“…In their seminal article, "Improving retrieval performance by relevance feedback", Salton and Buckley (1990) (Kaptein & Kamps, 2011;Xu, Luo, Yu, & Xu, 2011;Hamdi, 2011;Li, Otsuka, & Kitsuregawa, 2010;Fu, 2010;Gabrilovich et al, 2009;Nauer & Toussaint, 2009;Yumoto, Mori, & Sumiya, 2009;Kuppusamy & Aghila, 2009), Web commerce (Verma, Tiwari, & Mishra, 2011), Web 2.0 RSS feed content (Teng, Liu, & Ren, 2010), and multilingual IR (He & Wu, 2011;He, Tu, Luo, & Li, 2009;). …”