thinBE哈工大信息检索研究室.PPT

HITIR’s Update Summary at TAC2008Extractive Content Selection Using Evolutionary Manifold-ranking and Spectral Clustering Reporter: Ph.d candidate He Ruifang rfhe@ir.hit.edu.cn Information Retrieval Lab School of Computer Science and Technology Harbin Institute of Technology, Harbin, China Evaluation rank Three top 1 in PYRAMID average modified(pyramid) score average numSCUs macro-average modified score with 3 models of PYRAMID 13th in ROUGE-2 15th in ROUGE-SU4 17th in BE Update summary introduction Aims to capture evolving information of a single topic changing over time Temporal data can be considered to be composed of many time slices Question analysis from view of data First difference: data has the temporal evolution characteristic Deal with dynamic document collection of a single topic in continuous periods of time from view of users Second difference: user needs have evolution characteristic Hope to incrementally care the important and novel information relevant to a topic Challenges for update summary(extractive or generative) Content selection Importance Redundancy Content coverage Language quality Coherence Fluency Just focus on the extractive content selection How to model the importance and the redundancy of topic relevance and the content converge under the evolving data and user needs? Evolutionary manifold-ranking Evolutionary manifold-ranking Manifold-ranking ranks the data points under the intrinsic global manifold structure by their relevance to the query Difficulty: not model the temporally evolving characteristic, as the query is static ! Assumption of our idea Data points evolving over time have the long and narrow manifold structure Motivation of our idea Relay point of information propagation Dynamic evolution of query Relay propagation of information Iterative feedback mechanism in evolutionary manifold-ranking Th