summarization and visualization of digital conversations
DESCRIPTION
Paper presented at SPIM worksop at LREC2010, Malta.TRANSCRIPT
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Summarization and Visualization of Digital
Conversations
Vincenzo Pallotta�Joint work with�
Rodolfo Delmonte, University of Venice, Italy�Marita Ailomaa, EPFL, Switzerland�
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Digital Conversations
• The Web �– Social Media�– Forums�– Blogs �
• Meetings�• VoIP�• Call centers�• Help Desk �
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Captured Meetings
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Virtual Collaboration
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1st Hypothesis…
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V. Pallotta, Content-based retrieval of distributed multimedia conversational data. In E. Vargiu, A. Soro, G. Armano, G. Paddeu (eds.) Information Retrieval and Mining in Distributed Environments, Springer Verlag, series: Studies in Computational Intelligence (ISSN: 1860-949X) to Appear, 2010.
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Challenges for (spoken) conversation processing
• dealing with multiple speakers�• dealing with foreign language and associated
accents�• incorporating non-speech audio dialogue acts �
– (e.g., clapping, laughter, silence?)�• conversational segmentation and summarization �• discourse analysis, such as: �
– analyzing speaking rates�– turn taking (frequency, durations)�– concurrence/disagreement �
• which often provides insights into speaker emotional state, �– attitudes toward topics and other speakers�– roles/relationships.�
SPIM 2010 - Malta
M. Maybury: Keynote at the SIGIR 2007 Workshop Searching Spontaneous Conversational Speech
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Capturing and Processing Conversations
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• Informal Meetings • Focus Groups • Classes • Interviews • Debates • Podcasts • Comments • Forums
• Executive Summaries • Topic highlights • Issue tracking • Project management • Mediation • Semantic Search
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2nd Hypothesis…
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What type of content is user looking for from conversations?
• Users look for argumentative information �– Decision Making �– Conflict Resolution �
• Information Retrieval is not sufficient �– Need for more context �– Answers not found in
words spoken �
05
10152025303540
Factual Thematic Process Outcome
IM2 setMS set
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0102030
4050607080
IR sufficient IR irrelevant IR insufficient
IM2 set:argumentativeMS set:argumentative
Pallotta, Seretan, Ailomaa ACL 2007
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3rd Hypothesis…
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…in what form?
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…more demographic details
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…and still more
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4th Hypothesis…
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Two reviews from ACL…
• "The idea of using argument structure annotation to aid dialogue summarization is very promising. For an abstractive summary of dialogues this seems almost like an inevitable step and I am always glad to see people take on the hard task of abstractive summarization.“�
• "I think the general approach of detecting the argumentative structure is the correct one to take and the authors are laying groundwork for a solid abstractive system."�
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Our Approach…
• Topic Segmentation �• Recognition of argumentative episodes: �
– Based on the GETARUNS system�• Automatic recognition of argumentative
structure: �– Novel discourse parsing algorithm�
• Retrieval through: �– Question Answering �– Abstractive summaries �– Visualization of arguments�
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Meeting Description Schema DISCUSS(issue) <- PROPOSE(alternative) 1702.95 David: so - so my question is should we go ahead and get na- - nine identical head mounted crown mikes ? {qy} 61a
REJECT(alternative) 1708.89 John: not before having one come here and have some people try it out . {s^arp^co} 61b.62a
PROVIDE(justification) 1714.09 B: because there's no point in doing that if it's not going to be any better . {s} 61b+
ACCEPT(justification) 1712.69 David: okay . {s^bk} 62b
PROPOSE(alternative) 1716.85 John: so why don't we get one of these with the crown with a different headset ? {qw^cs} 63a
ACCEPT(alternative) 1721.56 David: yeah . {s^bk} 63b 1726.05 Lucy: yeah . {b} 1727.34 John: yeah . {b}
PROVIDE(justification) 1722.4 John: and - and see if that works . {s^cs} 63a+.64a 1723.53 Mark: and see if it's preferable and if it is then we'll get more . {s^cs^2} 64b 1725.47 Mark: comfort . {s}
PROVIDE(justification) 1714.09 John: because there's no point in doing that if it's not going to be any better . {s} 61b+
Why was David’s proposal on microphones rejected?
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Abstractive Summary
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• David proposal was: “go ahead and get nine identical head mounted crown mikes” • David’s proposal was rejected. • John provided an alternative: “get one of these with crown with a different headset”. John’s proposal was accepted by the majority of participants.
DISCUSS(issue) <- PROPOSE(alternative) 1702.95 David: so - so my question is should we go ahead and get na- - nine identical head mounted crown mikes ? {qy} 61a
REJECT(alternative) 1708.89 John: not before having one come here and have some people try it out . {s^arp^co} 61b.62a
PROVIDE(justification) 1714.09 B: because there's no point in doing that if it's not going to be any better . {s} 61b+
ACCEPT(justification) 1712.69 David: okay . {s^bk} 62b
PROPOSE(alternative) 1716.85 John: so why don't we get one of these with the crown with a different headset ? {qw^cs} 63a
ACCEPT(alternative) 1721.56 David: yeah . {s^bk} 63b 1726.05 Lucy: yeah . {b} 1727.34 John: yeah . {b}
PROVIDE(justification) 1722.4 John: and - and see if that works . {s^cs} 63a+.64a 1723.53 Mark: and see if it's preferable and if it is then we'll get more . {s^cs^2} 64b 1725.47 Mark: comfort . {s}
PROVIDE(justification) 1714.09 John: because there's no point in doing that if it's not going to be any better . {s} 61b+
• David proposal was: “go ahead and get nine identical head mounted crown mikes” • David’s proposal was rejected. • John provided an alternative: “get one of these with crown with a different headset”. John’s proposal was accepted by the majority of participants.
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Argumentative Labeling with GETARUNS
• Primitive Discourse Relations labels: �– statement, narration, adverse, result,
cause, motivation, explanation, question, hypothesis, elaboration, permission, inception, circumstance, obligation, evaluation, agreement, contrast, evidence, hypoth, setting, prohibition. �
• Mapped into Argumentative labels: �– ACCEPT, REJECT/DISAGREE, PROPOSE/
SUGGEST, EXPLAIN/JUSTIFY, REQUEST EXPLANATION/JUSTIFICATION.�
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Delmonte R., Bistrot A., Pallotta V.,Deep Linguistic Processing with GETARUNS for spoken dialogue Understanding. Proceedings LREC 2010 (P31 Dialogue Corpora).
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Evaluation
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Correct Incorrect Total Found Precision
Accept 662 16 678 98%
Reject 64 18 82 78%
Propose 321 74 395 81%
Request 180 1 181 99%
Explain 580 312 892 65%
Disfluency 19 0 19 100%
Total 1826 421 2247 81%
Precision: 81.26% Recall: 97.53%
ICSI corpus of meetings (Janin et al., 2003)
Delmonte R., Bistrot A., Pallotta V.,Deep Linguistic Processing with GETARUNS for spoken dialogue Understanding. Proceedings LREC 2010 (P31 Dialogue Corpora).
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Applications for Visualization and Summarization of Digital Conversations
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Conversational Graphs
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[7:00] # Yes, uh, I've a question, uh, what's mean exactly advance chip on print? What's the meaning of that? [7:10] 7 5 [7:02] Yeah [7:2] [7:10] I think it's um uh a multiple uh chip design uh and it's maybe printed on to the (curcuit) board. [7:20] 8 7 [7:21] Mm-hmm. [7:21] [7:21] Uh I could find out more about that uh before the next fi- next meeting. [7:26] 8.1 8 [7:24] Yeah, is it means it's on the - x#x is it on the micro-processor based or uh - [7:30] 9 8 [7:32] I don't know, but I'll find out more on our next meeting. [7:35] 10 11 11:09
[7:34] [O]okay, uh, that would be great, so if you find out from the technology backgroud, okay, so that would be good[.] [7:39] 12 10 [7:39] Sounds good. [7:40] [7:41] Why was the plastic eliminated as a possible material? [7:44] 13 3 [7:43] Because um it gets brittle - [7:46] 14 13 3 [7:47] cracks - [7:48] 14 13 3 [7:48] uh-huh [7:49] [7:51] um [7:51] 14 13 3 [7:53] We want - we expect these um these remote controls to be around for several hundred years. [7:59] 14 13 3
[8:00] So $ we could $ (??) - good expression [8:6] [8:02] (I would gi-) [8:2] [8:02] Wow $ Good expression, (well) after us $ [8:12] [8:05] Which - [8:6]
[8:12] Um, speak for yourself, I (??) $ - [8:16] [8:13] Alth- I think - [8:15] [8:14] $ [8:16]
[8:16] I think with the wood though you'd run into the same types of problems (??) I mean it chips, it- if you drop it, ehm, it's - I'm not su- $ [8:27] 15 16
15:14 (15:3?) 16:15
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Mapping to Bales IPA categories
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Improving Opinion Mining
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Attitude scores re-ranking
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NESTLÉ� twittrratr� Interanalytics� Δ�
Positive� 13% � 34% � 21% ��
Neutral� 85% � 40% � -45% �
Negative� 3% � 16% � 13% �Not Clear� 0% � 10% � 10% �Total� 100% � 100%�
Reliability Scores� 33% � 80% �
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Abstractive Summaries of Digital Conversations
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Conversation Memos (1)
GENERAL INFORMATION ON PARTICIPANTS�• The participants to the meeting are 7.�• Participants less actively involved are Ami and Don who
only intervened respectively for 38 and 68 turns.�
LEVEL OF INTERACTIVITY IN THE DISCUSSION �• The speaker that has held the majority of turns is
Adam with a total of 722 turns, followed by Fey with a total of 561.�
• The speaker that has undergone the majority of overlaps is Adam followed by Jane.�
• The speaker that has done the majority of overlaps is Jane followed by Fey.�
• Jane is the participant that has been most competitive.�
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Conversation Memos (2)
DISCUSSION TOPICS�• The discussion was centered on the following topics: �" "schemas, action, things and domain.�
• The main topics have been introduced by the most important speaker of the meeting. �
• The participant who introduced the main topics in the meeting is: Adam.�
• The most frequent entities in the whole dialogue partly coincide with the best topics, and are the following: �action, schema, things, 'source-path-goal', person, spg, roles,
bakery, intention, specific, case, categories, information, idea.�
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Conversation Memos (3)
ARGUMENTATIVE CONTENT�The following participants: �
"Andreas, Dave, Don, Jane, Morgan �expressed their dissent 52 times. However
Dave, Andreas and Morgan expressed dissent in a consistently smaller percentage.�
The following participants: �"Adam, Andreas, Dave, Don, Jane, Morgan �
asked questions 55 times.�The remaining 1210 turns expressed positive
content by proposing, explaining or raising issues. However Adam, Dave and Andreas suggested and raised new issues in a consistently smaller percentage.�
The following participants: Adam, Andreas, Dave, Don, Jane, Morgan expressed acceptance 213 times.�
�
EPISODE ISSUE No. 7�In this episode we have the following
argumentative exchanges between the following speakers: Don, Morgan.�
Morgan provides the following explanation: �[oh, that-s_, good, .] �then he , overlapped by Don, continues: �[because, we, have, a_lot, of, breath, noises, .] �
Don accepts the previous explanation: �[yep, .] �
then he provides the following explanation: �[test, .] �
Morgan continues: �[in_fact, if, you, listen, to, just, the, channels, of, people,
not, talking, it-s_, like, ..., .] �
then he , overlapped by Don, disagrees with the previous explanation �
[it-s_, very, disgust, ..., .] �
Don, overlapped by Morgan, asks the following question: �
[did, you, see, hannibal, recently, or, something, ?] �
Morgan provides the following positive answer: �[sorry, .] �
then he provides the following explanation: �[exactly, .] �[it-s_, very, disconcerting, .] �[okay, .] �
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Conclusion
• Conversational Search and Condensation is extremely challenging �– Classical approaches simply don’t work �– Sense-making is needed�
• One possible “sense”: �– Argumentative structure�
• Possible outputs: �– Question Answering �– Abstractive Summaries�– Conversation Graphs�
• Future Work: �– Improving performance of the classifier�– Build the linking structure of arguments�– Approach generation �
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Summarization and Visualization of Digital
Conversations
Vincenzo Pallotta�Joint work with�
Rodolfo Delmonte & Marita Ailomaa�