From Arguments to Key Points: Towards Automatic Argument Summarization
| Autor: | Roy Bar-Haim, Noam Slonim, Dan Lahav, Yoav Kantor, Roni Friedman, Lilach Eden |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0303 health sciences 03 medical and health sciences Information retrieval Computer Science - Computation and Language 030306 microbiology Computer science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Computation and Language (cs.CL) Automatic summarization |
| Zdroj: | ACL |
| Popis: | Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed "key points", each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task. We report empirical results for an extensive set of experiments with this dataset, showing promising performance. ACL 2020 |
| Databáze: | OpenAIRE |
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