1. An Approach to Abstractive Multi-Entity Summarization
龚赛赛

2. Contents
Background and Motivation
Related Work
Method Framework

3. 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)

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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 persons age 28+ .

10. 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

11. 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.

12. Evaluation Plan Entity set: an entity with its neighbors
Invite expert to make guided summary
Compare automatic summary with reference

13. Evaluation Plan

14. 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