1) The document proposes extracting a more specific discussion structure called "Oppose and IBIS structure" from online discussion forums to allow automated facilitation agents to perform more diverse facilitations. 2) This structure adds an "Oppose structure" element to the existing IBIS structure, and can increase the number of potential facilitations from 80 to 107. 3) The authors define a method to extract nodes using a tree structure classifier and restrict link extraction to sentences within 20 distances, achieving better performance than existing methods in experiments on real discussion data.

1. Increasing Facilitations
by Extracting Concrete Discussion Structures
The 15th International Conference
on Knowledge, Information and Creativity Support Systems
Nagoya Institute of Technology, Japan
Naoki Kawamura, Shun Okuhara, Takayuki Ito

2. Background
Online Discussion Support System: D-Agree [1]
n The facilitator plays an important role for various participants
to make effective discussions on online forums
• To manage discussions
• To promote opinions
n D-Agree has Automated Facilitation Agent [2] to support discussions
Automated Facilitation Agent
n Automated facilitation agent could overcome limitations of human
facilitator
• The agents facilitate the ongoing discussions without rest
• The agents consider the opinions that human can not understand
n Automated facilitation agent is running, with using estimated discussion
structure
2
[1] Takayuki Ito et al. D-agree: Crowd discussion support system based on automated facilitation agent. In Proceedings of 35th AAAI
conference, volume 2020, 2020. demonstration paper.
[2] Takayuki Ito et al. Agent that facilitates crowd discussion. In Proceedings of ACM Collective Intelligence, volume 2019, 2019.

3. Background
Issue-Based Information System (IBIS)[3]
n An approach for structuring discussions
n The elements of the IBIS
• Issues: need to be answered
• Ideas: possible answers
• Pros: support an idea
• Cons: object to an idea
n Nodes are issues, ideas, pros, and cons
n Links are the relationships amongst
the nodes
n Automated facilitation agent adopt the IBIS in order to manage
online discussions
3
[3] Kunz, Werner, and Horst WJ Rittel. Issues as elements of information systems. Vol.
131. Berkeley, California: Institute of Urban and Regional Development, University of
California, 1970.
idea
pros cons
issues
Figure : Showing nodes and links
of IBIS structure

4. Problem
Issue_PN
Positive NegativeMiddle
Reason Reason
idea
pros cons
issues
What is your position on the issue?
Using IBIS Using another structure
What do you think about this opinion?
Do you agree or disagree,
about the restart of nuclear power plants?
Do you agree or disagree,
about the restart of nuclear power plants?
Abstract Facilitation Concrete Facilitation
Fail to focus on
“agree or disagree”
Success to focus on
“agree or disagree”
NO OK

5. Objective
Increases the number of facilitations
n IBIS structure is abstract
→ Agents could not perform more diverse facilitations
n We extract a more specific discussion structure
• Extracting nodes in a more specific structure
• Extracting links in a more specific structure
- Predicting the links amongst extracted nodes
We define a more specific structure.
We increase the number of facilitations.
Agent can be running with extracted specific discussion structure.

6. Define a more specific structure
Define “Oppose and IBIS structure”
n We add an “Oppose structure” to IBIS structure to get a more
specific discussion structure
n The elements of the Oppose structure
• Issue_PN: asks for agreement or disagreement
• Positive: representing position of agreement
• Middle: representing neutral position
• Negative: representing position of disagreement
• Reason: shows the reason for the position
Issue_PN
Positive NegativeMiddle
Reason Reason
Figure : Showing nodes and links of Oppose structure

7. Increased number of facilitations
n We can increase as many as 27 facilitations
7
IBIS Oppose and IBIS
issue 31 31
Ideas 42 42
pros 4 4
cons 3 3
Issue_PN 9
Positive 3
Middle 7
Negative 4
Reason 4
Sum 80 107
TABLE
The number of facilitations

8. Related Works:
Suzuki’s Extracting Nodes Approach
n Suzuki’s approach[4] employs bidirectional long short-term
memory (Bi-LSTM)[5] which is a type of recurrent neural network
(RNN) model
n Suzuki's node approach gets normalized probabilities
8
morpheme fastText
Let 0.9 0.2 … 0.8
us 0.8 0.5 … 0.2
discuss 0.6 0.1 … 0.3
town 0.2 0.9 … 0.1
development 0.3 0.2 … 0.7
with 0.7 0.3 … 0.5
IoT 0.5 0.1 … 0.8
and 0.4 0.9 … 0.2
AI 0.1 0.7 … 0.4
issues ideas pros cons
0.83 0.12 0.02 0.03
Bi-
LSTM
Dense
[4] Shota SUZUKI et al. Extrac- tion of online discussion structures for automated facilitation agent. Proceedings of
the Annual Conference of JSAI, JSAI2019:2F1E302–2F1E302, 2019.
[5] Sepp Hochreiter and Schmidhuber. Long short-term memory. Neural Comput., 9(8):1735–1780, November 1997.
Figure : Suzuki’s node extraction architecture[4]

9. Extracting Nodes in Our Research
Tree structure
n Tree structure is assumed to classify similar nodes
n To classify step by step, we make any classifiers
n We classify some nodes as same label in first classifier
n We classify some nodes to reveal Oppose and IBIS nodes in second classifier
Figure : structure of tree

10. Related Works:
Suzuki’s Extracting Links Approach
n Suzuki’s approach calculates the cosine similarities between the
output and the candidate head nodes.
10
sentence fastText
I 0.2 0.8 … 0.7
propose 0.5 0.1 … 0.2
signs 0.3 0.9 … 0.8
that 0.9 0.4 … 0.5
provide 0.4 0.3 … 0.1
information 0.1 0.7 … 0.3
Bi-LSTM
regression
0.7 0.3 … 0.9
0.9 0.1 … 0.3
0.4 0.8 … 0.1
0.2 0.6 … 0.7
0.1 0.2 … 0.2
0.5 0.7 … 0.4
0.3 0.4 … 0.6
0.2 0.9 … 0.8
0.8 0.5 … 0.5
sentence fastText
Let 0.9 0.2 … 0.8
us 0.8 0.5 … 0.2
discuss 0.6 0.1 … 0.3
town 0.2 0.9 … 0.1
development 0.3 0.2 … 0.7
with 0.7 0.3 … 0.5
IoT 0.5 0.1 … 0.8
and 0.4 0.9 … 0.2
AI 0.1 0.7 … 0.4
sentence fastText
what 0.3 0.6 … 0.5
moral 0.2 0.1 … 0.3
AI 0.7 0.1 … 0.9
should 0.4 0.7 … 0.1
have 0.3 0.5 … 0.1
0 0 … 0
0 0 … 0
0 0 … 0
0 0 … 0
sentence fastText
How 0.5 0.6 … 0.4
to 0.3 0.5 … 0.7
reduce 0.1 0.2 … 0.6
disaster 0.3 0.8 … 0.9
0 0 … 0
0 0 … 0
0 0 … 0
0 0 … 0
0 0 … 0
cosine similarity:0.34
cosine similarity:0.13
cosine similarity:0.72
choose
Let’s
discuss town development
with IoT and AI.
I propose signs that
provide information.
How to reduce disaster?
What moral AI
should have?
?
Figure : Suzuki’s link extraction architecture[4]

11. Extracting Links in Our Research
Restrict links
n We calculates the cosine similarities like Suzuki’s extraction links
n In this research, excluding candidate nodes that human can not
consider by restricting , unlike Suzuki’s extraction links
• It is estimated that we can extract links even if there are many
similar opinions in discussions
n We restrict links by the distance of sentences up to 20
11

12. Experiments
Datasets
n Online discussions with Japanese that were created in D-Agree
n 9 discussions
n The themes connected with Hamaoka Nuclear Power Plant
Num nodes
issue 70
Ideas 260
pros 100
cons 130
Issue_PN 50
Positive 130
Middle 10
Negative 170
Reason 60
Num links
idea->issue
180
pros->idea
100
cons->idea
130
Positive
->IssuePN
130
Negative
->IssuePN
170
Middle
->IssuePN
20
Reason
->Positive
19
Reason
->Negative
30

13. Results of Nodes Experimental
Results of the structure of tree
• 10-fold-cross-validation
• The average values of F1 score
13
Num node Tree structure Suzuki’s model
issues 70 0.73 0.73
Ideas 260 0.57 0.61
pros 100 0.45 0.48
cons 130 0.44 0.40
Issue_PN 50 0.56 0.54
Positive 130 0.43 0.38
Middle 10 0.00 0.00
Negative 170 0.45 0.43
Reason 60 0.52 0.52

14. Results of Links Experimental
Results of Link Extraction
• 10-fold-cross-validation
• The average values of precision
14
Num links
closeness no limit
precision Num links
closeness up to 20
precision
idea->issue 180 0.25 138 0.71
pros->idea 100 0.18 100 0.31
cons->idea 130 0.10 130 0.32
Positive->Issue PN 130 0.42 75 0.83
Negative->Issue PN 170 0.56 150 0.86
Middle->Issue PN 20 0.55 9 0.70
Reason->Positive 19 0.30 20 0.55
Reason->Negative 30 0.27 30 0.37

15. Conclusion
Objective
• Increasing facilitations by extracting concrete discussion structures
Define concrete discussion structures
• We define the “Oppose and IBIS structure” instead of abstract IBIS structure
→ We can increase as many as 27 facilitations
Extracting Nodes and Links in Oppose and IBIS Structure
• We make tree structure to classify similar definition nodes
• Restrict links by the distance of sentences up to 20
Experiments
• We gathered the experimental Japanese data from online discussions that
were created in D-Agree
Results
• The experimental results demonstrate the proposed approach is efficient for
extracting Oppose and IBIS
15