1. Relatedness-based Multi-Entity Summarization
Kalpa Gunaratna1, Amir Hossein Yazdavar1, Krishnaprasad Thirunarayan1,
Amit Sheth1, and Gong Cheng2
1Kno.e.sis Center, Wright State University, Dayton OH, USA
2National Key Laboratory for Novel Software Technology, Nanjing University, China

2. Reading news online
Within one month of the iPod nano and iTunes
phone special event, Apple Computer announced
today another special event to be held on
October 12. It is to be held at the California
Theater in downtown San Jose, California. The
invitation reads, “One more thing…”, the teasing
tagline of Steve Jobs.

3. Text enriched by entity linking
with knowledge bases
Within one month of the iPod nano and iTunes
phone special event, Apple Computer announced
today another special event to be held on
October 12. It is to be held at the California
Theater in downtown San Jose, California. The
invitation reads, “One more thing…”, the teasing
tagline of Steve Jobs.

4. Text enriched by entity linking
with knowledge bases
Within one month of the iPod nano and iTunes
phone special event, Apple Computer announced
today another special event to be held on
October 12. It is to be held at the California
Theater in downtown San Jose, California. The
invitation reads, “One more thing…”, the teasing
tagline of Steve Jobs.
Apple Inc. is an American
multinational technology
company headquartered in
Cupertino, California that …
such as a textual pop-up
from Wikipedia

5. Text enriched by entity linking
with knowledge bases
Within one month of the iPod nano and iTunes
phone special event, Apple Computer announced
today another special event to be held on
October 12. It is to be held at the California
Theater in downtown San Jose, California. The
invitation reads, “One more thing…”, the teasing
tagline of Steve Jobs.
or a structured pop-up
from DBpedia or Wikidata
founders:Steve_jobs
product: IPod
location: California
industry: Consumer_electronics

6. An entity can have several hundred
property-value pairs (called features).
…
…

7. An entity can have several hundred
property-value pairs (called features).
…
…
An entity summary is a subset of k features (to fit in a pop-up).

8. Entity summarization
(previous research)
• Problem statement (k=3, in this example)

9. Entity summarization
(previous research)
• Problem statement (k=3, in this example)
• Research challenge
• To select important and diverse features

10. Text enriched by entity linking
with knowledge bases
Within one month of the iPod nano and iTunes
phone special event, Apple Computer announced
today another special event to be held on
October 12. It is to be held at the California
Theater in downtown San Jose, California. The
invitation reads, “One more thing…”, the teasing
tagline of Steve Jobs.
founders:Steve_jobs
product: IPod
location: California
industry: Consumer_electronics

11. Text enriched by entity linking
with knowledge bases
Within one month of the iPod nano and iTunes
phone special event, Apple Computer announced
today another special event to be held on
October 12. It is to be held at the California
Theater in downtown San Jose, California. The
invitation reads, “One more thing…”, the teasing
tagline of Steve Jobs.
founders:Steve_jobs
product: IPod
location: California
industry: Consumer_electronics
after: Tim_Cook
knownFor: Microcomputer_revolution
title: Apple_Inc.
birthPlace: California

12. Text enriched by entity linking
with knowledge bases
Within one month of the iPod nano and iTunes
phone special event, Apple Computer announced
today another special event to be held on
October 12. It is to be held at the California
Theater in downtown San Jose, California. The
invitation reads, “One more thing…”, the teasing
tagline of Steve Jobs.
founders:Steve_jobs
product: IPod
location: California
industry: Consumer_electronics
after: Tim_Cook
knownFor: Microcomputer_revolution
title: Apple_Inc.
birthPlace: California

13. Multi-entity summarization
• Problem statement (n=3, k=3, in this example)
• New research challenge
• To select intra-entity important and diverse features
• To select inter-entity related features
i.e., context-dependent entity summarization (context: other entities)

14. Multi-entity summarization
• Problem statement (n=3, k=3, in this example)
• New research challenge
• To select intra-entity important and diverse features
• To select inter-entity related features
i.e., context-dependent entity summarization (context: other entities)

15. Multi-entity summarization
• Problem statement (n=3, k=3, in this example)
• New research challenge
• To select intra-entity important and diverse features
• To select inter-entity related features
i.e., context-dependent entity summarization (context: other entities)

16. • Multi-entity summarization as a
Quadratic Multidimensional Knapsack Problem (QMKP),
to jointly generate summaries for n entities
Problem formulation

17. • Multi-entity summarization as a
Quadratic Multidimensional Knapsack Problem (QMKP),
to jointly generate summaries for n entities
Problem formulation
Profit earned by selecting a feature f:
Intra-entity importance of f

18. • Multi-entity summarization as a
Quadratic Multidimensional Knapsack Problem (QMKP),
to jointly generate summaries for n entities
Problem formulation
Profit earned by selecting a feature f:
Intra-entity importance of f
Profit earned by selecting two features
fi and fj describing the same entity:
Intra-entity dissimilarity between fi and fj

19. • Multi-entity summarization as a
Quadratic Multidimensional Knapsack Problem (QMKP),
to jointly generate summaries for n entities
Problem formulation
Profit earned by selecting two features
fi and fj describing the same entity:
Intra-entity dissimilarity between fi and fj
Profit earned by selecting a feature f:
Intra-entity importance of f
Profit earned by selecting two features
fi and fj describing different entities:
Inter-entity relatedness between fi and fj

20. • Multi-entity summarization as a
Quadratic Multidimensional Knapsack Problem (QMKP),
to jointly generate summaries for n entities
Problem formulation
Profit earned by selecting two features
fi and fj describing the same entity:
Intra-entity dissimilarity between fi and fj
Profit earned by selecting a feature f:
Intra-entity importance of f
Profit earned by selecting two features
fi and fj describing different entities:
Inter-entity relatedness between fi and fj
n constraints:
Selecting at most k features for each of the n entities

21. Solution
• A GRASP algorithm for QMKP
• Measures
• Importance of a feature (i.e., property-value pair):
Informativeness of feature * popularity of value
• Dissimilarity between two features:
Negative semantic similarity
• Relatedness between two features:
Semantic similarity

22. Solution
• A GRASP algorithm for QMKP
• Measures
• Importance of a feature (i.e., property-value pair):
Informativeness of feature * popularity of value
• Dissimilarity between two features:
Negative semantic similarity
• Relatedness between two features:
Semantic similarity WordNet-based similarity between properties +
graph-embedding-based similarity between values

23. User study
• 2 datasets on news, linked with entities in DBpedia
• 3 entity summarizers
• REMES: our multi-entity summarizer
• FACES & RELIN: two state-of-the-art entity summarizers

24. Take-home messages
• Multi-entity summarization (MES)
• finds applications (e.g., news item presentation)
• faces new research challenges (e.g., inter-entity relatedness)
• needs specialized approaches
• Future work
• Improved methods for relatedness-based MES
• Extended methods for novel applications of MES