An Approach to Abstractive Multi-Entity Summarization 龚赛赛 2015-06-23
Contents Background and Motivation Related Work Method Framework
Background Multi-entity summarization: distill the major information from a set of entities to generate a compressed summary Useful for many tasks, including entity search and entity browsing Potential for QA (cp. multi-document summarization for QA)
Motivation Existing works mainly generate extractive summaries for an entity set, usually an entity pair, to find certain similarities, differences or identity information Extract a subset of sentences/features/property-value pairs for different entities Not efficient for identifying similarities and differences for a set of more than 2 entities
Motivation Example Better summary using count All the 23 persons affiliation Websoft 3 status teachers, 5 status phdstudents, … 15 of 23 from jiangsu 3 deliver www paper … …… Affiliation:websoft …… Affiliation:websoft Affiliation:websoft Affiliation:websoft status: Professor status : Professor status: AP status: phd student status: phd student …… …… From: jiangsu From: jiangsu From: jiangsu From: jiangsu From: jiangsu Deliver www paper Deliver www paper Deliver www paper
Motivation Generate abstractive summary for finding commonality (similarity) of the entire set and particularity(difference) of each entity Using count Using subclass,subproperty reasoning Using literal value partitioning
Summary UI Commonality panel Difference panel (focus on difference) … All the 23 persons affiliation Websoft 3 status teachers, 5 status phdstudents, … 15 of 23 from jiangsu … Deliver 2 www papers Leader
Related work Single entity summarization ([1,3]) Find important information Based on centrality, relatedness, informativeness, uniqueness and so on Consider diversity using linear programming, MMR or clustering Entity pair summarization ([2]) Identify similarities and differences based on uniqueness and property-value similarity/dissimilarity Consider diversity Entity set ([4]) Identify uniqueness, differences and contextual relatedness for facilitating entity linking Structured summary Path for entity pairs [6], Steiner tree for input entities [5], connected subgraph for input entities [7] Not condensable
Method Framework Two summaries: Commonality summary for identifying the major common information of the set (entire vs subset), and single summary for identifying the particularity of a single entity Single summary: extract top-K property-value pairs Informal commonality summary statement ? Given the entity set with their descriptions, generate top-K “facets”? Facets allowing entity num count, literal value interval ? A facet consists of a property with its value combination? 20 students. 5 are of phd students. 9 are of msc students. 5 persons age 23-26 . 3 persons age 28+ .
Commonality Summary Value combination e.g. For types, get superclasses. Select suitable classes to group/partition entities For literals, find suitable intervals to group/partition entities Handle equivalent/synonymous props Replace to a representative one Basic idea for generating top-k facets Covered entity number The group number of entities inducing from the facet Property importance
Single Summary Remove property-value pairs that are similar to the facets in commonality summary Extract property-value pairs as summary based on informativeness and uniqueness.
Evaluation Plan Entity set: an entity with its neighbors Invite expert to make guided summary Compare automatic summary with reference
Evaluation Plan
Reference FACES: Diversity-Aware Entity Summarization using Incremental Hierarchical Conceptual Clustering. aaai Facilitating human intervention in coreference resolution with comparative entity summaries. eswc Relin: relatedness and informativeness-based centrality for entity summarization. iswc Summarizing Entity Descriptions for Effective and Efficient Human-centered Entity Linking. www STAR: Steiner Tree Approximation in Relationship-Graphs Rex:Explaining relationships between entity pairs Fast discovery of connection subgraphs