- Good abandonment – where user’s leave search engine without reading any webpage, because the answer is provided in the SERP.
- non-factoid queries are more frequently asked on the web.
- Past work – to provide passage level answers to non-factoid queries.
- Summarization could be better – because answers might be in different sentences scattered in the underlying document.
- Answer biased summary – extracting a summary from each retrieved document that is expected to contain answers to a non-factoid query.
- Designed to hint at the whereabout of likely answers.
- Using Community Question Answering (CQA) content to guide the extraction of answer-biased summaries.
- Why bother if CQA is present? a) better summaries than CQA answers. b) even imperfect CQA answers can help find summaries, c) learning to rank based model to help extract summaries even where CQA answers are not available.
- Novel user of CQA content in a summarization algorithm for locating answer-bearing sentences in the document
- 3 optimization based methods and a learning to rank based method for answering non-factoid queries.
- Analyse the effect of CQA quality on such methods.
The paper then goes on to propose 3 optimization methods using CPLEX. There is some discussion of the learning model, to use when CQA is not available.
Interestingly there is no mention of Knowledge Graphs being used. Though query expansion could be done using KGs.
I have been trying to use FAQs which are not too different from CQAs. So overall a very interesting paper.