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Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data

Gkatzia, Dimitra; Hastie, Helen; Lemon, Oliver


Helen Hastie

Oliver Lemon


We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label (ML)
classification problem, which takes as input time-series data and outputs a set of templates, while capturing the dependencies between selected templates. We show that this method generates output closer to the feedback that lecturers actually generated, achieving 3.5% higher accuracy and 15% higher F-score than multiple simple classifiers that keep a history of selected templates. Furthermore, we compare a ML classifier with a Reinforcement Learning (RL) approach in simulation and using ratings from real student users. We show that the different methods have different benefits, with ML being more
accurate for predicting what was seen in the training data, whereas RL is more exploratory and slightly preferred by the students.

Presentation Conference Type Conference Paper (Published)
Conference Name The 52nd Annual Meeting of the Association for Computational Linguistics
Start Date Jun 22, 2014
End Date Jun 27, 2014
Publication Date 2014
Deposit Date Aug 1, 2016
Publisher Association for Computational Linguistics (ACL)
Pages 1231-1240
Book Title Proceedings of the Conference Volume 1: Long Papers
ISBN 9781937284725
Keywords Reinforcement Learning, RL, multi-label, ML.
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