A Summarization System for Scientific Documents
We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes...
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| creator | Erera, Shai Shmueli-Scheuer, Michal Feigenblat, Guy Nakash, Ora Peled Boni, Odellia Roitman, Haggai Cohen, Doron Weiner, Bar Mass, Yosi Rivlin, Or Lev, Guy Jerbi, Achiya Herzig, Jonathan Hou, Yufang Jochim, Charles Gleize, Martin Bonin, Francesca Konopnicki, David |
| description | We present a novel system providing summaries for Computer Science
publications. Through a qualitative user study, we identified the most valuable
scenarios for discovery, exploration and understanding of scientific documents.
Based on these findings, we built a system that retrieves and summarizes
scientific documents for a given information need, either in form of a
free-text query or by choosing categorized values such as scientific tasks,
datasets and more. Our system ingested 270,000 papers, and its summarization
module aims to generate concise yet detailed summaries. We validated our
approach with human experts. |
| doi_str_mv | 10.48550/arxiv.1908.11152 |
| format | Article |
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publications. Through a qualitative user study, we identified the most valuable
scenarios for discovery, exploration and understanding of scientific documents.
Based on these findings, we built a system that retrieves and summarizes
scientific documents for a given information need, either in form of a
free-text query or by choosing categorized values such as scientific tasks,
datasets and more. Our system ingested 270,000 papers, and its summarization
module aims to generate concise yet detailed summaries. We validated our
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publications. Through a qualitative user study, we identified the most valuable
scenarios for discovery, exploration and understanding of scientific documents.
Based on these findings, we built a system that retrieves and summarizes
scientific documents for a given information need, either in form of a
free-text query or by choosing categorized values such as scientific tasks,
datasets and more. Our system ingested 270,000 papers, and its summarization
module aims to generate concise yet detailed summaries. We validated our
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publications. Through a qualitative user study, we identified the most valuable
scenarios for discovery, exploration and understanding of scientific documents.
Based on these findings, we built a system that retrieves and summarizes
scientific documents for a given information need, either in form of a
free-text query or by choosing categorized values such as scientific tasks,
datasets and more. Our system ingested 270,000 papers, and its summarization
module aims to generate concise yet detailed summaries. We validated our
approach with human experts.</abstract><doi>10.48550/arxiv.1908.11152</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.48550/arxiv.1908.11152 |
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| language | eng |
| recordid | cdi_arxiv_primary_1908_11152 |
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| subjects | Computer Science - Computation and Language |
| title | A Summarization System for Scientific Documents |
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