There exists an ever-growing set of data-centric systems that allow data scientists of varying skill levels to interactively manipulate, analyze and explore large structured data sets. However, there are currently not many systems that allow data scientists and novice users to interactively explore large unstructured text document collections from heterogeneous sources.

Therefore, we present a new system for interactive text summarization called Sherlock. The task of automatically producing textual summaries is an important step to understand a collection of multiple topic-related documents. It has many real-world applications in journalism, medicine, and many more. However, none of the existing summarization systems allow users to provide feedback at interactive speed. We therefore integrate a new approximate summarization model into Sherlock that can guarantee interactive speeds even for large text collections to keep the user engaged in the process.

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Foto Benjamin Hättasch


P. V. S., Avinesh (2020):
Information Preparation with the Human in the Loop.
Darmstadt, TU Darmstadt,
DOI: 10.25534/tuprints-00011839,

Hättasch, Benjamin ; Meyer, Christian M. ; Binnig, Carsten (2019):
Interactive Summarization of Large Document Collections.
In: HILDA'19: Proceedings of the ..., S. 1-4,
Amsterdam, Niederlande, Workshop on Human-In-the-Loop Data Analytics, Amsterdam, 05.07.2019, ISBN 978-1-4503-6791-2,
DOI: 10.1145/3328519.3329129,

Hättasch, Benjamin
Alonso, Omar ; Silvello, Gianmaria (Hrsg.) (2018):
Towards Interactive Summarization of Large Document Collections.
S. 103, Bertinoro, Italy, CEUR Workshop Proceedings, First Biennial Conference on Design of Experimental Search & Information Retrieval Systems, Bertinoro, Italy, 28.-31.08.2018, [Konferenzveröffentlichung]

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P. V. S., Avinesh ; Hättasch, Benjamin ; Özyurt, Orkan ; Binnig, Carsten ; Meyer, Christian M. (2018):
Sherlock: A System for Interactive Summarization of Large Text Collections.
11, In: Proceedings of the VLDB Endowment, S. 1902-1905,
The 44th International Conference on Very Large Data Bases (VLDB 2018), Rio De Janerio, Brazil, 27.08.2018--31.08.2018, DOI: 10.14778/3229863.3236220,

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