from social bookmarking to summarization

Views:
 
Category: Entertainment
     
 

Presentation Description

No description available.

Comments

Presentation Transcript

From Social Bookmarking to Social Summarization: An Experiment in Community-Based Summary Generation: 

From Social Bookmarking to Social Summarization: An Experiment in Community-Based Summary Generation Oisin Boydell, Barry Smyth Adaptive Information Cluster, School of Computer Science and Informatics University College Dublin 2007 Intelligent User Interfaces Presented by Sharon HSIAO 2007.10.05

Agenda: 

Agenda Introduction Novelty way to generate a social summary Evaluation & Methodology Experiments Discussion Conclusion

Introduction: 

Introduction Traditional approach of summarization technique may perform well in general; however, it may not meet the needs and preferences of individual users or a community of users, to extract the core content of the document effectively

Summarization: 

Summarization 2 broad approaches to summarization: Extraction Open Text Summarizer (OTS) MEAD Summarizer Word occurrence and positional information to extract high scoring sentences Abstraction Rely heavily on syntactic Representation is conceptual

Web page Summarization: 

Web page Summarization Html markup In-linking text Search engine click-through Sentence-selection algorithm: web content+query click-through the weight of query words is increased according to its frequency within the query collection Social summarization interaction or usage data can be used to good effect to generate high quality summaries of Web pages

idea of Social Summarization: 

idea of Social Summarization 1. A page p can be associated with a set of queries, Q(p) =q1, . . . , qn 2. For a given query, qi, the search engine (SE) will produce a query-sensitive snippet, SSE(p, qi), which contains a number of sentence fragments 3. The social summary for p, SSSE(p), can be constructed from the combination of fragments associated with Q(p) according to the importance of the fragment, give rank order

Generating a social summary : 

Generating a social summary extract the snippet texts, S(bi, p) to produce a set of sentence fragments normalise sentence fragments to cope with fragment overlap and subsumption score each sentence fragment according to its frequency of occurrence across the snippets rank-order the normalised fragments to produce the final summary

Setup & Methodology: 

Setup & Methodology Data from Del.icio.us 3781 bookmarked pages Tags up to a maximum 50 per page 1386 pages contained description text within HMTL meta-content description tag Compared with OTS and MEAD Lucene snippet generator (Apache Foundation) ROUGE(Recall-Oriented Understudy for Gisting Evaluation): to compare generated to gold-standard; counting overlapping n-gram, word sequences, word pairs

Experiment 1 Comparison of Summary Quality: 

Experiment 1 Comparison of Summary Quality Avg length of SS summaries was 24% of the original

Experiment 2 Summary Length vs. Quality: 

Experiment 2 Summary Length vs. Quality consider the quality of summaries of different lengths, by eliminating low scoring fragments from the final social summary

Experiment 3 Search Activity vs Quality: 

Experiment 3 Search Activity vs Quality consider the relationship between the number of available cues (bookmark tags, in this case) and summary quality query sets of size 1-10, 11-20, 21-30, 31-40, and 41-50 queries selected randomly, producing nearly 25,000 different summaries in total

Slide13: 

SS produces summaries with recall scores that are 31% better than the OTS summaries and approximately 28% better than the MEAD summaries

Discussion: 

Discussion Query-Focused Social Summaries generating a more focused social summary that is informed perhaps by the context provided by some target user query, SS(p, qT ) top ranking results may be associated with longer (more detailed) social summaries than lower ranking results

Slide15: 

Community-Focused Social Summaries social summarization technique can be used to generate query focused snippets that better reflect the niche needs of a particular community of searchers identify those queries that have led to the past selection of p by community members and that are similar to qT Eg. “Jaguar parts” “Genuine Jaguar, Land Rover and Range Rover OEM and brand name aftermarket parts” “The one-stop-shop for genuine restoration Jaguar parts for all classic models including S Type, X Type, X300 - XJR, ...”

Preliminary results: 

Preliminary results extracted the top 100 bookmarked pages for tag “travel” Then extracted the top bookmark tags used to label each of these pages; generate a new set of tags (eg. European travel, travel tips…) 1153 bookmarked pages, 5291 unique sets of terms, 6290 unique users Training & test set 5 random split training&test

Conclusion: 

Conclusion social summarization technique produces higher-quality summaries query-focused social summaries provide searchers with improved result-snippet summaries community-focused summaries — summaries that better reflect the needs of communities of like minded users