Aggregating Multi-Resources to Improve the Diversity of Search Engine Result Pages
Date Issued
2014
Date
2014
Author(s)
Chiang, Chung-Lun
Abstract
Previous work on snippet generation focused mainly on how to produce one snippet for an individual search result. This paper aims to generate snippets as a comprehensive overview for an entity query (e.g., flu) in a search-result page.
Our approach first extracts the attributes (e.g., symptom and diagnose) of the categories (e.g., disease) from multi-resources including a community-based question-answering (CQA) website, an online encyclopedia website and suggestions from a commercial search engine. Then, we generate the snippets based on how central a sentence is to the query, its category, and how well it diversifies the attributes from multi-resources. Integer Linear Programming (ILP) is adopted to find the optimal sentence set. After finding the initial set of sentences, we further improve the result by aggregate the search-result page(SERP) of the query''s suggestion words.
The experiments are conducted on Wikipedia, Yahoo! Answers, Google Search. Experimental results demonstrate the effectiveness of our approach, compared to an existing commercial search engine and several summarization baselines.
Subjects
搜尋結果總結
片段資訊產生
搜尋詞概要
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-103-R01922007-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):593ed1d889b22e61116692b2b6a3e26e