Advertisement

Summarizing Answers for Community Question Answer Services

  • Vinay Pande
  • Tanmoy Mukherjee
  • Vasudeva Varma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8105)

Abstract

This paper presents a novel answer summarization approach for community Question Answering services (cQAs) to address the problem of “incomplete answer”, i.e., missing valuable information from the “best answer” of a complex multi-sentence question, which can be obtained from other answers to the same question. Our method automatically generate a novel and non-redundant summary from cQA answers using structured determinantal point processes (SDPP). Experimental evaluation on sample dataset from Yahoo Answers shows significant improvement over baseline approaches.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adamic, L.A., Zhang, J., Bakshy, E., Ackerman, M.S.: Knowledge sharing and yahoo answers: everyone knows something. In: Proceedings of WWW (2008)Google Scholar
  2. Chan, W., Zhou, X., Wang, W., Chua, T.-S.: Community answer summarization for multi-sentence question with group l1 regularization. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers. Association for Computational Linguistics, vol. 1, pp. 582–591 (2012)Google Scholar
  3. Cong, G., Wang, L., Lin, C.-Y., Song, Y.-I., Sun, Y.: Finding question-answer pairs from online forums. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 467–474. ACM (2008)Google Scholar
  4. Ding, S., Cong, G., Lin, C.Y., Zhu, X.: Using conditional random fields to extract contexts and answers of questions from online forums. In: Proceedings of ACL 2008: HLT (2008)Google Scholar
  5. Gillenwater, J., Kulesza, A., Taskar, B.: Discovering diverse and salient threads in document collections. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, pp. 710–720 (2012)Google Scholar
  6. Kulesza, A., Taskar, B.: Structured determinantal point processes (2010)Google Scholar
  7. Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out: Proceedings of the ACL 2004 Workshop, pp. 74–81 (2004)Google Scholar
  8. Liu, Y., Bian, J., Agichtein, E.: Predicting information seeker satisfaction in community question answering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 483–490. ACM (2008)Google Scholar
  9. Shen, D., Sun, J.-T., Li, H., Yang, Q., Chen, Z.: Document summarization using conditional random fields. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, vol. 7, pp. 2862–2867 (2007)Google Scholar
  10. Tomasoni, M., Huang, M.: Metadata-aware measures for answer summarization in community question answering. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 760–769 (2010)Google Scholar
  11. Wang, K., Ming, Z.-Y., Hu, X., Chua, T.-S.: Segmentation of multi-sentence questions: towards effective question retrieval in cqa services. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 387–394. ACM (2010)Google Scholar
  12. Wang, H., Wang, C., Zhai, C., Han, J.: Learning online discussion structures by conditional random fields. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 435–444. ACM (2011)Google Scholar
  13. Yang, Z., Cai, K., Tang, J., Zhang, L., Su, Z., Li, J.: Social context summarization. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 255–264. ACM (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vinay Pande
    • 1
  • Tanmoy Mukherjee
    • 1
  • Vasudeva Varma
    • 1
  1. 1.International Institute of Information TechnologyHyderabadIndia

Personalised recommendations