Feature Selection for Summarising: The Sunderland DUC 2004 Experience

In this paper we describe our participation in task 1-very short single-document summaries in DUC 2004. The task chosen is related to our research project, which aims to produce abstracting summaries to improve search engine result summaries. DUC allowed us to produce summaries no longer than 75 characters, therefore we focused on feature selection to produce a set of key words as summaries instead of complete sentences. Three descriptions of our summarisers are given. Each of the summarisers performs very differently in the six ROUGE metrics. One of our summarisers which uses a simple algorithm to produce summaries without any supervised learning or complicated NLP technique performs surprisingly well among different ROUGE evaluations. Finally we give an analysis of ROUGE and participants’ results. ROUGE is an automatic evaluation of summaries package, which uses n-gram matching to calculate the overlapping between machine and human summaries, and indeed saves time for human evaluation. However, the different ROUGE metrics give different results and it is hard to judge which is the best for automatic summaries evaluation. Also it does not include complete sentences evaluation. Therefore we suggest some work needs to be done on ROUGE in the future to make it really effective

Searching for Non-English Web Content: An Empirical Study of the Spanish Business Intelligence Portal

As non-English-speaking online populations grow rapidly, there are increasing needs to support searching for non-English Web content. Prior research has assumed English to be the primary language for Web searching, but this is not the case for many non-English-speaking regions. For example, Latin America will have the fastest growing population in the coming decades but existing Spanish search engines lack search, browse, and analysis capabilities. In this paper, we have proposed a language-independent approach to supporting non-English Web searching. Based on the approach, we have developed the Spanish Business Intelligence Portal (SBizPort) to support searching, browsing, summarization, categorization, and visualization of Web information. Results from an empirical study involving Spanish subjects show that the portal achieved significantly better user ratings on information quality, cross-regional search capability, and overall satisfaction than the benchmark search portal. This study thus contributes to human-computer interaction research on non-English Web searching

CiteFinder: a System to Find and Rank Medical Citations

This thesis presents CiteFinder, a system to find relevant citations for clinicians\u27 written content. Inclusion of citations for clinical information content makes the content more reliable through the provision of scientific articles as references, and enables clinicians to easily update their written content using new information. The proposed approach splits the content into sentences, identifies the sentences that need to be supported with citations by applying classification algorithms, and uses information retrieval and ranking techniques to extract and rank relevant citations from MEDLINE for any given sentence. Additionally, this system extracts snippets from the retrieved articles. We assessed our approach on 3,699 MEDLINE papers on the subject of Heart Failure . We implemented multi-level and weight ranking algorithms to rank the citations. This study shows that using Journal priority and Study Design type significantly improves results obtained with the traditional approach of only using the text of articles, by approximately 63%. We also show that using the full-text, rather than just the abstract text, leads to extraction of higher quality snippets

Building Knowledge Management System for Researching Terrorist Groups on the Web

Nowadays, terrorist organizations have found a cost-effective resource to advance their courses by posting high-impact Web sites on the Internet. This alternate side of the Web is referred to as the “Dark Web.” While counterterrorism researchers seek to obtain and analyze information from the Dark Web, several problems prevent effective and efficient knowledge discovery: the dynamic and hidden character of terrorist Web sites, information overload, and language barrier problems. This study proposes an intelligent knowledge management system to support the discovery and analysis of multilingual terrorist-created Web data. We developed a systematic approach to identify, collect and store up-to-date multilingual terrorist Web data. We also propose to build an intelligent Web-based knowledge portal integrated with advanced text and Web mining techniques such as summarization, categorization and cross-lingual retrieval to facilitate the knowledge discovery from Dark Web resources. We believe our knowledge portal provide counterterrorism research communities with valuable datasets and tools in knowledge discovery and sharing

Automatic text summarisation using linguistic knowledge-based semantics

Text summarisation is reducing a text document to a short substitute summary. Since the commencement of the field, almost all summarisation research works implemented to this date involve identification and extraction of the most important document/cluster segments, called extraction. This typically involves scoring each document sentence according to a composite scoring function consisting of surface level and semantic features. Enabling machines to analyse text features and understand their meaning potentially requires both text semantic analysis and equipping computers with an external semantic knowledge. This thesis addresses extractive text summarisation by proposing a number of semantic and knowledge-based approaches. The work combines the high-quality semantic information in WordNet, the crowdsourced encyclopaedic knowledge in Wikipedia, and the manually crafted categorial variation in CatVar, to improve the summary quality. Such improvements are accomplished through sentence level morphological analysis and the incorporation of Wikipedia-based named-entity semantic relatedness while using heuristic algorithms. The study also investigates how sentence-level semantic analysis based on semantic role labelling (SRL), leveraged with a background world knowledge, influences sentence textual similarity and text summarisation. The proposed sentence similarity and summarisation methods were evaluated on standard publicly available datasets such as the Microsoft Research Paraphrase Corpus (MSRPC), TREC-9 Question Variants, and the Document Understanding Conference 2002, 2005, 2006 (DUC 2002, DUC 2005, DUC 2006) Corpora. The project also uses Recall-Oriented Understudy for Gisting Evaluation (ROUGE) for the quantitative assessment of the proposed summarisers’ performances. Results of our systems showed their effectiveness as compared to related state-of-the-art summarisation methods and baselines. Of the proposed summarisers, the SRL Wikipedia-based system demonstrated the best performance