Summarizing Dialogic Arguments from Social Media
Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Compact, easy to read, summaries of these dialogues would thus be highly valuable. A priori, it is not even clear what form...
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| creator | Misra, Amita Oraby, Shereen Tandon, Shubhangi TS, Sharath Anand, Pranav Walker, Marilyn |
| description | Online argumentative dialog is a rich source of information on popular
beliefs and opinions that could be useful to companies as well as governmental
or public policy agencies. Compact, easy to read, summaries of these dialogues
would thus be highly valuable. A priori, it is not even clear what form such a
summary should take. Previous work on summarization has primarily focused on
summarizing written texts, where the notion of an abstract of the text is well
defined. We collect gold standard training data consisting of five human
summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control
and Abortion. We present several different computational models aimed at
identifying segments of the dialogues whose content should be used for the
summary, using linguistic features and Word2vec features with both SVMs and
Bidirectional LSTMs. We show that we can identify the most important arguments
by using the dialog context with a best F-measure of 0.74 for gun control, 0.71
for gay marriage, and 0.67 for abortion. |
| doi_str_mv | 10.48550/arxiv.1711.00092 |
| format | Article |
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beliefs and opinions that could be useful to companies as well as governmental
or public policy agencies. Compact, easy to read, summaries of these dialogues
would thus be highly valuable. A priori, it is not even clear what form such a
summary should take. Previous work on summarization has primarily focused on
summarizing written texts, where the notion of an abstract of the text is well
defined. We collect gold standard training data consisting of five human
summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control
and Abortion. We present several different computational models aimed at
identifying segments of the dialogues whose content should be used for the
summary, using linguistic features and Word2vec features with both SVMs and
Bidirectional LSTMs. We show that we can identify the most important arguments
by using the dialog context with a best F-measure of 0.74 for gun control, 0.71
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beliefs and opinions that could be useful to companies as well as governmental
or public policy agencies. Compact, easy to read, summaries of these dialogues
would thus be highly valuable. A priori, it is not even clear what form such a
summary should take. Previous work on summarization has primarily focused on
summarizing written texts, where the notion of an abstract of the text is well
defined. We collect gold standard training data consisting of five human
summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control
and Abortion. We present several different computational models aimed at
identifying segments of the dialogues whose content should be used for the
summary, using linguistic features and Word2vec features with both SVMs and
Bidirectional LSTMs. We show that we can identify the most important arguments
by using the dialog context with a best F-measure of 0.74 for gun control, 0.71
for gay marriage, and 0.67 for abortion.</description><subject>Computer Science - Computation and Language</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzs1OAjEUhuFuWBjwAlzZG5jx9L9dEsCfBOMC9pNjezppwjCkCFGvXkRX3-JNvjyM3QlotTcGHrB-lnMrnBAtAAR5w2BzGgas5bvse74suBv7Evm89qeB9h9Hnus48M0YL4W_Uio4Y5OMuyPd_u-UbR9X28Vzs357elnM1w1aJxubKEenNVoKOr9j0hhdUN6SUilI4SCAp2CTcBKFlkZGo2Qw3mUgj6Sm7P7v9kruDrVclF_dL7270tUPGqc9UQ</recordid><startdate>20171031</startdate><enddate>20171031</enddate><creator>Misra, Amita</creator><creator>Oraby, Shereen</creator><creator>Tandon, Shubhangi</creator><creator>TS, Sharath</creator><creator>Anand, Pranav</creator><creator>Walker, Marilyn</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20171031</creationdate><title>Summarizing Dialogic Arguments from Social Media</title><author>Misra, Amita ; Oraby, Shereen ; Tandon, Shubhangi ; TS, Sharath ; Anand, Pranav ; Walker, Marilyn</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a672-6defc744a6e94fbad4ac79386e33d92170908e96d172a14252c5329587f0e8ae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computer Science - Computation and Language</topic><toplevel>online_resources</toplevel><creatorcontrib>Misra, Amita</creatorcontrib><creatorcontrib>Oraby, Shereen</creatorcontrib><creatorcontrib>Tandon, Shubhangi</creatorcontrib><creatorcontrib>TS, Sharath</creatorcontrib><creatorcontrib>Anand, Pranav</creatorcontrib><creatorcontrib>Walker, Marilyn</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Misra, Amita</au><au>Oraby, Shereen</au><au>Tandon, Shubhangi</au><au>TS, Sharath</au><au>Anand, Pranav</au><au>Walker, Marilyn</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Summarizing Dialogic Arguments from Social Media</atitle><date>2017-10-31</date><risdate>2017</risdate><abstract>Online argumentative dialog is a rich source of information on popular
beliefs and opinions that could be useful to companies as well as governmental
or public policy agencies. Compact, easy to read, summaries of these dialogues
would thus be highly valuable. A priori, it is not even clear what form such a
summary should take. Previous work on summarization has primarily focused on
summarizing written texts, where the notion of an abstract of the text is well
defined. We collect gold standard training data consisting of five human
summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control
and Abortion. We present several different computational models aimed at
identifying segments of the dialogues whose content should be used for the
summary, using linguistic features and Word2vec features with both SVMs and
Bidirectional LSTMs. We show that we can identify the most important arguments
by using the dialog context with a best F-measure of 0.74 for gun control, 0.71
for gay marriage, and 0.67 for abortion.</abstract><doi>10.48550/arxiv.1711.00092</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computation and Language |
| title | Summarizing Dialogic Arguments from Social Media |
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