Invisible to People but not to Machines: Evaluation of Style-aware HeadlineGeneration in Absence of Reliable Human Judgment

Lorenzo De Mattei, Michele Cafagna, Felice Dell’Orletta, Malvina Nissim


Abstract
We automatically generate headlines that are expected to comply with the specific styles of two different Italian newspapers. Through a data alignment strategy and different training/testing settings, we aim at decoupling content from style and preserve the latter in generation. In order to evaluate the generated headlines’ quality in terms of their specific newspaper-compliance, we devise a fine-grained evaluation strategy based on automatic classification. We observe that our models do indeed learn newspaper-specific style. Importantly, we also observe that humans aren’t reliable judges for this task, since although familiar with the newspapers, they are not able to discern their specific styles even in the original human-written headlines. The utility of automatic evaluation goes therefore beyond saving the costs and hurdles of manual annotation, and deserves particular care in its design.
Anthology ID:
2020.lrec-1.828
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6709–6717
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.828
DOI:
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.828.pdf