Paper Info
Title
Question Headline Generation for News Articles.
Abstract
In this paper, we introduce and tackle the Question Headline Generation (QHG) task. The motivation comes from the investigation of a real-world news portal where we find that news articles with question headlines often receive much higher click-through ratio than those with non-question headlines. The QHG task can be viewed as a specific form of the Question Generation (QG) task, with the emphasis on creating a natural question from a given news article by taking the entire article as the answer. A good QHG model thus should be able to generate a question by summarizing the essential topics of an article. Based on this idea, we propose a novel dual-attention sequence-to-sequence model (DASeq2Seq) for the QHG task. Unlike traditional sequence-to-sequence models which only employ the attention mechanism in the decoding phase for better generation, our DASeq2Seq further introduces a self-attention mechanism in the encoding phase to help generate a good summary of the article. We investigate two ways of the self-attention mechanism, namely global self-attention and distributed self-attention. Besides, we employ a vocabulary gate over both generic and question vocabularies to better capture the question patterns. Through the offline experiments, we show that our approach can significantly outperform the state-of-the-art question generation or headline generation models. Furthermore, we also conduct online evaluation to demonstrate the effectiveness of our approach using A/B test.
Year
DOI
Venue
2018
10.1145/3269206.3271711
CIKM
Keywords
Field
DocType
Question headline generation, self-attention mechanism
Headline,Information retrieval,Computer science,Decoding methods,Online evaluation,Question generation,Vocabulary,Encoding (memory)
Conference
ISBN
Citations 
PageRank 
978-1-4503-6014-2
0
0.34
References 
Authors
34
7
Name
Order
Citations
PageRank
Ruqing Zhang11510.40
Jiafeng Guo21737102.17
Yixing Fan320219.39
Yanyan Lan4100563.59
Jun Xu501.01
Huanhuan Cao600.34
Xueqi Cheng73148247.04