TY - GEN
T1 - Stage-wise Stylistic Headline Generation
T2 - 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
AU - Zhan, Jiaao
AU - Gao, Yang
AU - Bai, Yu
AU - Liu, Qianhui
N1 - Publisher Copyright:
© 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2022
Y1 - 2022
N2 - A quality headline with a high click-rate should not only summarize the content of an article, but also reflect a style that attracts users. Such demand has drawn rising attention to the task of stylistic headline generation (SHG). An intuitive method is to first generate plain headlines leveraged by document-headline parallel data then transfer them to a target style. However, this inevitably suffers from error propagation. Therefore, to unify the two sub-tasks and explicitly decompose style-relevant attributes and summarize content, we propose an end-to-end stage-wise SHG model containing the style generation component and the content insertion component, where the former generates stylistic-relevant intermediate outputs and the latter receives these outputs then inserts the summarized content. The intermediate outputs are observable, making the style generation easy to control. Our system is comprehensively evaluated by both quantitative and qualitative metrics, and it achieves state-of-the-art results in SHG over three different stylistic datasets.
AB - A quality headline with a high click-rate should not only summarize the content of an article, but also reflect a style that attracts users. Such demand has drawn rising attention to the task of stylistic headline generation (SHG). An intuitive method is to first generate plain headlines leveraged by document-headline parallel data then transfer them to a target style. However, this inevitably suffers from error propagation. Therefore, to unify the two sub-tasks and explicitly decompose style-relevant attributes and summarize content, we propose an end-to-end stage-wise SHG model containing the style generation component and the content insertion component, where the former generates stylistic-relevant intermediate outputs and the latter receives these outputs then inserts the summarized content. The intermediate outputs are observable, making the style generation easy to control. Our system is comprehensively evaluated by both quantitative and qualitative metrics, and it achieves state-of-the-art results in SHG over three different stylistic datasets.
UR - http://www.scopus.com/inward/record.url?scp=85137883311&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85137883311
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 4489
EP - 4495
BT - Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
A2 - De Raedt, Luc
A2 - De Raedt, Luc
PB - International Joint Conferences on Artificial Intelligence
Y2 - 23 July 2022 through 29 July 2022
ER -