Predicting sentential semantic compatibility for aggregation in text-to-text generation

Victor Chenal, Jackie Chi Kit Cheung


Abstract
We examine the task of aggregation in the context of text-to-text generation. We introduce a new aggregation task which frames the process as grouping input sentence fragments into clusters that are to be expressed as a single output sentence. We extract datasets for this task from a corpus using an automatic extraction process. Based on the results of a user study, we develop two gold-standard clusterings and corresponding evaluation methods for each dataset. We present a hierarchical clustering framework for predicting aggregation decisions on this task, which outperforms several baselines and can serve as a reference in future work.
Anthology ID:
C16-1101
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1061–1070
Language:
URL:
https://aclanthology.org/C16-1101
DOI:
Bibkey:
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PDF:
https://aclanthology.org/C16-1101.pdf