2 research outputs found
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
Current summarization systems only produce plain, factual headlines, but do
not meet the practical needs of creating memorable titles to increase exposure.
We propose a new task, Stylistic Headline Generation (SHG), to enrich the
headlines with three style options (humor, romance and clickbait), in order to
attract more readers. With no style-specific article-headline pair (only a
standard headline summarization dataset and mono-style corpora), our method
TitleStylist generates style-specific headlines by combining the summarization
and reconstruction tasks into a multitasking framework. We also introduced a
novel parameter sharing scheme to further disentangle the style from the text.
Through both automatic and human evaluation, we demonstrate that TitleStylist
can generate relevant, fluent headlines with three target styles: humor,
romance, and clickbait. The attraction score of our model generated headlines
surpasses that of the state-of-the-art summarization model by 9.68%, and even
outperforms human-written references.Comment: ACL 202
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
Two important tasks at the intersection of knowledge graphs and natural
language processing are graph-to-text (G2T) and text-to-graph (T2G) conversion.
Due to the difficulty and high cost of data collection, the supervised data
available in the two fields are usually on the magnitude of tens of thousands,
for example, 18K in the WebNLG~2017 dataset after preprocessing, which is far
fewer than the millions of data for other tasks such as machine translation.
Consequently, deep learning models for G2T and T2G suffer largely from scarce
training data. We present CycleGT, an unsupervised training method that can
bootstrap from fully non-parallel graph and text data, and iteratively back
translate between the two forms. Experiments on WebNLG datasets show that our
unsupervised model trained on the same number of data achieves performance on
par with several fully supervised models. Further experiments on the
non-parallel GenWiki dataset verify that our method performs the best among
unsupervised baselines. This validates our framework as an effective approach
to overcome the data scarcity problem in the fields of G2T and T2G. Our code is
available at https://github.com/QipengGuo/CycleGT.Comment: INLG 2020 Worksho