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)
doi: 10.1145/3328519.3329129
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Hättasch, Benjamin ; Meyer, Christian M. ; Binnig, Carsten |
| Art des Eintrags: | Bibliographie |
| Titel: | Interactive Summarization of Large Document Collections |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2019 |
| Ort: | Amsterdam, Niederlande |
| Buchtitel: | HILDA'19: Proceedings of the ... |
| Veranstaltungstitel: | Workshop on Human-In-the-Loop Data Analytics |
| Veranstaltungsort: | Amsterdam |
| Veranstaltungsdatum: | 05.07.2019 |
| DOI: | 10.1145/3328519.3329129 |
| URL / URN: | https://hilda.io/2019/proceedings/HILDA2019_paper_4.pdf |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Text Summarization, Machine Learning, Approximate Computing, AIPHES_area_d2, dm, dm_vi_ml, dm_sherlock |
| Zusätzliche Informationen: | Article No 9 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Data Management (2022 umbenannt in Data and AI Systems) DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 26 Apr 2019 13:27 |
| Letzte Änderung: | 22 Apr 2020 07:41 |
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