Summarizing Denition from Wikipedia Articles Zhicheng Zheng Xiaoyan

Summarizing Denition from. Wikipedia Articles Zhicheng Zheng, Xiaoyan Zhu Tsinghua University 清华大学计算机系

Outline • Introduction • Related work • Method • Experiments • Conclusion 清华大学计算机系

Outline • Introduction • Related work • Method • Experiments • Conclusion 清华大学计算机系

Introduction • “Definition” from Wikipedia – A definition is a passage describing the meaning of a term (a word, phrase or other set of symbols), or a type of thing. • “Definition” treated in TREC-QA – “What is XXX” or “Who is XXX” – Or translated to “Tell something important or interesting about XXX” 清华大学计算机系

Outline • Introduction • Related work • Method • Experiments • Conclusion 清华大学计算机系

Related work • Pattern based methods [1, 2] – Using patterns to generate potential definitional sentences – Advantages: accurate – Disadvantages: require manual labor or labeled data to generate patterns 清华大学计算机系

Related work • Relevance based methods – interesting nuggets often come in the form of trivia, novel or rare facts about the topic that tend to strongly co-occur with direct mention of topic keywords [3] – Using summarization techniques – Using Wikipedia articles [4] • Usingle article 清华大学计算机系

Related work • Using multiple Wikipedia articles? – Benefits: improve summarization performance • users would better understand a topic if they read more related articles 清华大学计算机系

Outline • Introduction • Related work • Method • Experiments • Conclusion 清华大学计算机系

Method • Representation Model – Represent articles/sentences as vector of words/concepts • Word Model: TF/IDF – Not accurate enough – “Jordan Hill“ vs “Michael Jordan”, “Grant Hill” • Wikipedia Concept Model 清华大学计算机系

• Wikipedia concept model – Wikipedia concept: Title of Wikipedia article – Represent a piece of text with the Wikipedia concepts in the text • Similar as TF/IDF way of word model 清华大学计算机系

Method • Wikipedia article expansion – Extract all related Wikipedia articles with main Wikipeida article • d and d’ are related d and d’ link with each other 清华大学计算机系

Method • Summarizing definitions – Constraint: select sentences only from the original article (not from the other related articles) – Summarization method: • Maximal Marginal Relevance (MMR) [5] • Maximum Coverage (MC) [6] 清华大学计算机系

Outline • Introduction • Related work • Method • Experiments • Conclusion 清华大学计算机系

Experiment • Wikipedia articles: – Snapshots of English Wikipedia (2009) • Question sets: – Questions from TREC 13 – 15 – 215 definition questions • 190 could be found in Wikipedia • Evaluation metrics – Precision, Recall, F 1, F 3 清华大学计算机系

Experiment • Results – Compare: • Word model vs. Wikipeida concept model • Single article vs. Multiple articles 清华大学计算机系

Experiments • Analysis – Both the two algorithms benefit from the Wikipedia concept model – The related article set can help improving the performance in both the two algorithms. – The Wikipedia concept model contributes more to precision than to recall – The related article set leads to more improvement in terms of nugget recall than the Wikipedia concept model 清华大学计算机系

Conclusion • A method of summarizing definition from multiple Wikipedia articles – Experiments show than Wikipedia concepts benefits the extraction of definition – Also show that the related articles help weight concepts more effectively 清华大学计算机系

Thank you Any questions? 清华大学计算机系

References [1] Xu, J. , Licuanan, A. , Weischedel, R. : Trec 2003 qa at bbn: Answering definitional questions. In: TREC 2003. [2] Cui, H. , Kan, M. Y. , Chua, T. S. : Generic soft pattern models for definitional question answering. In: SIGIR 2005. [3] Kor, K. W. , Chua, T. S. : Interesting nuggets and their impact on definitional question answering. In: SIGIR 2007 [4] Ye, S. , Chua, T. S. , Lu, J. : Summarizing definition fromwikipedia. In: ACL 2009 [5] Carbonell, J. , Goldstein, J. : The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998 [6] Gillick, D. , Favre, B. : A scalable global model for summarization. In: ACL 2009 清华大学计算机系