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

ACL ID P14-1116
Title Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We present a novel approach for automatic report generation from time-series data, in the context of student feedback genera- tion. Our proposed methodology treats content selection as a multi-label (ML) classification problem, which takes as in- put time-series data and outputs a set of templates, while capturing the dependen- cies between selected templates. We show that this method generates output closer to the feedback that lecturers actually gener- ated, 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 Learn- ing (RL) approach in simulation and using ratings from real student users. We show that the different methods have different benefits, wi...