Hättasch, Benjamin ; Meyer, Christian M. ; Binnig, Carsten (2019)
Interactive Summarization of Large Document Collections.
Workshop on Human-In-the-Loop Data Analytics. Amsterdam (05.07.2019-05.07.2019)
doi: 10.1145/3328519.3329129
Conference or Workshop Item, Bibliographie
Abstract
We present a new system for custom summarizations of large text corpora at interactive speed. The task of producing textual summaries is an important step to understand large collections of topicrelated documents and has many real-world applications in journalism, medicine, and many more. Key to our system is that the summarization model is refined by user feedback and called multiple times to improve the quality of the summarization iteratively. To that end, the human is brought into the loop to gather feedback in every iteration about which aspects of the intermediate summaries satisfy their individual information needs. Our system consists of a sampling component and a learned model to produce a textual summary. As we show in our evaluation, our system can provide a similar quality level as existing summarization models that are working on the full corpus and hence cannot provide interactive speeds.
Item Type: | Conference or Workshop Item |
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Erschienen: | 2019 |
Creators: | Hättasch, Benjamin ; Meyer, Christian M. ; Binnig, Carsten |
Type of entry: | Bibliographie |
Title: | Interactive Summarization of Large Document Collections |
Language: | English |
Date: | July 2019 |
Place of Publication: | Amsterdam, Niederlande |
Book Title: | HILDA'19: Proceedings of the ... |
Event Title: | Workshop on Human-In-the-Loop Data Analytics |
Event Location: | Amsterdam |
Event Dates: | 05.07.2019-05.07.2019 |
DOI: | 10.1145/3328519.3329129 |
URL / URN: | https://hilda.io/2019/proceedings/HILDA2019_paper_4.pdf |
Abstract: | We present a new system for custom summarizations of large text corpora at interactive speed. The task of producing textual summaries is an important step to understand large collections of topicrelated documents and has many real-world applications in journalism, medicine, and many more. Key to our system is that the summarization model is refined by user feedback and called multiple times to improve the quality of the summarization iteratively. To that end, the human is brought into the loop to gather feedback in every iteration about which aspects of the intermediate summaries satisfy their individual information needs. Our system consists of a sampling component and a learned model to produce a textual summary. As we show in our evaluation, our system can provide a similar quality level as existing summarization models that are working on the full corpus and hence cannot provide interactive speeds. |
Uncontrolled Keywords: | Text Summarization, Machine Learning, Approximate Computing, AIPHES_area_d2, dm, dm_vi_ml, dm_sherlock |
Additional Information: | Article No 9 |
Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Data Management (2022 umbenannt in Data and AI Systems) DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources |
Date Deposited: | 26 Apr 2019 13:27 |
Last Modified: | 22 Apr 2020 07:41 |
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