‘Show me more’: Incremental length summarisation using novelty detection

Sweeney, Simon; Crestani, Fábio; Losada, David E.

doi:10.1016/j.ipm.2007.03.012

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…In order to generate update summaries, the approach described in Sweeney et al (2008) consists of incorporating novelty to summaries, by minimizing the content overlap between a summary sentence and a potential candidate one. In Witte et al (2007), Bellemare et al (2008), Li et al (2009) update summaries are built on the basis of cluster graph data structures, which are based on the context and on the set of documents that are going to be summarized.…”

Section: Update Summariesmentioning

confidence: 99%

Text summarisation in progress: a literature review

2011

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This paper contains a large literature review in the research field of Text Summarisation (TS) based on Human Language Technologies (HLT). TS helps users manage the vast amount of information available, by condensing documents' content and extracting the most relevant facts or topics included in them. The rapid development of emerging technologies poses new challenges to this research field, which still need to be solved. Therefore, it is essential to analyse its progress over the years, and provide an overview of the past, present and future directions, highlighting the main advances achieved and outlining remaining limitations. With this purpose, several important aspects are addressed within the scope of this survey. On the one hand, the paper aims at giving a general perspective on the state-ofthe-art, describing the main concepts, as well as different summarisation approaches, and relevant international forums. Furthermore, it is important to stress upon the fact that the birth of new requirements and scenarios has led to new types of summaries with specific purposes (e.g. sentiment-based summaries), and novel domains within which TS has proven to be also suitable for (e.g. blogs). In addition, TS is successfully combined with a number of intelligent systems based on HLT (e.g. information retrieval, question answering, and text classification). On the other hand, a deep study of the evaluation of summaries is also conducted in this paper, where the existing methodologies and systems are explained, as well as new research that has emerged concerning the automatic evaluation of summaries' quality. Finally, some thoughts about TS in general and its future will encourage the reader to think of novel approaches, applications and lines to conduct research in the next years. The analysis of these issues allows the reader to have a wide and useful background on the main important aspects of this research field.

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Section: Update Summariesmentioning

confidence: 99%

Text summarisation in progress: a literature review

2011

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“…In the Novelty Detection Track, the most similar task to story tracking is the Novel Sentence Retrieval Task in which the goal is to retrieve sentences previously judged as "new" by human judges. Various text-summarization methods such as [5,6] and sentence-retrieval methods such as [21,37] have been applied to these tasks.…”

Section: Related Workmentioning

confidence: 99%

Story graphs: Tracking document set evolution using dynamic graphs

Subasic

Berendt

2013

IDA

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With the growing number of document sets accessible online, tracking their evolution over time (story tracking) became an increasingly interesting problem. In this paper we propose a story tracking method based on the dynamics of keyword-association graphs. We create a graph representation of the story evolution that we call story graphs, and investigate how graph structure can be used for detecting and discovering new developments in the story. First we investigate the possibly interesting graph properties for development detection. We continue by investigating how graph structure can be linked to the sentences representing developments. For this we create an evaluation framework which bridges the gap between temporal text mining patterns and sentences. We apply this framework to evaluate our method against other temporal text mining methods. Our experiments show that story graphs perform at similar levels overall, but provide distinctive advantages in some settings.

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“…ND at the sentence level has many similarities with that of summarization studies. In both of them, only the necessary sentences should be delivered to the user (Sweeney, Crestani, & Losada, 2008). In summarization, there also is a necessity to compress the given text, which is not valid for ND studies in TREC.…”

Section: Related Workmentioning

confidence: 99%

Novelty detection for topic tracking

Aksoy

Can

Kocberber

2011

Acta Anaesthesiol Scand

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Multisource web news portals provide various advantages such as richness in news content and an opportunity to follow developments from different perspectives. However, in such environments, news variety and quantity can have an overwhelming effect. New-event detection and topic-tracking studies address this problem. They examine news streams and organize stories according to their events; however, several tracking stories of an event/topic may contain no new information (i.e., no novelty). We study the novelty detection (ND) problem on the tracking news of a particular topic. For this purpose, we build a Turkish ND test collection called BilNov-2005 and propose the usage of three ND methods: a cosine-similarity (CS)-based method, a language-model (LM)-based method, and a cover-coefficient (CC)-based method. For the LM-based ND method, we show that a simpler smoothing approach, Dirichlet smoothing, can have similar performance to a more complex smoothing approach, Shrinkage smoothing. We introduce a baseline that shows the performance of a system with random novelty decisions. In addition, a category-based threshold learning method is used for the first time in ND literature. The experimental results show that the LM-based ND method significantly outperforms the CS-and CC-based methods, and categorybased threshold learning achieves promising results when compared to general threshold learning. IntroductionThe Internet has changed the news industry (The Economist, 2011). Most newspapers and news agencies provide news on their web pages. News portals work as a news aggregator and gather, merge, and organize news articles obtained from various sources. Multisource news portals provide various advantages such as richness in news content and an opportunity to follow event developments from different perspectives. In addition, it is practical to Received May 20, 2011; revised September 12, 2011; accepted September 28, 2011 1 Present address: Computer Science Department, New Jersey Institute of Technology, University Heights Newark, NJ 07102.© 2011 ASIS&T • Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/asi.21697 follow different news sources from a single web page. Google News (http://news.google.com) is a well-known commercial news portal example. It offers many services such as information retrieval, personalized information filtering, and news clustering. Research-oriented examples include NewsBlaster (McKeown et al., 2002) and NewsInEssence (Radev, Otterbacher, Winkel, & Blair-Goldensohn, 2005), each of which provides clustering and summarization services over the news.As the number of sources and events increase, news readers may be overloaded with information and thus may face difficulty in finding news related to their interests. Different organizational techniques have been employed for more effective, efficient, and enjoyable browsing. Studies on new-event detection and topic tracking aim to organize news with respect to events or topics. In topic detection and trackin...

show abstract

‘Show me more’: Incremental length summarisation using novelty detection

Cited by 10 publications

References 29 publications

Text summarisation in progress: a literature review

Text summarisation in progress: a literature review

Story graphs: Tracking document set evolution using dynamic graphs

Novelty detection for topic tracking

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