Abstract
We propose a global optimization method
under length constraint (GOLC) for neural
text summarization models. GOLC increases
the probabilities of generating summaries that
have high evaluation scores, ROUGE in this
paper, within a desired length. We compared GOLC with two optimization methods, a maximum log-likelihood and a minimum risk training, on CNN/Daily Mail and a
Japanese single document summarization data
set of The Mainichi Shimbun Newspapers.
The experimental results show that a state-ofthe-art neural summarization model optimized
with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6.70% overlength summaries
on CNN/Daily and 7.8% on long summary
of Mainichi, compared to the approximately
20% to 50% on CNN/Daily Mail and 10% to
30% on Mainichi with the other optimization
methods. We also demonstrate the importance
of the generation of in-length summaries for
post-editing with the dataset Mainich that is
created with strict length constraints. The experimental results show approximately 30% to
40% improved post-editing time by use of inlength summaries