  Knowledge of the creation date of documents facilitates several tasks such as
summarization, event extraction, temporally focused information extraction etc.
Unfortunately, for most of the documents on the Web, the time-stamp metadata is
either missing or can't be trusted. Thus, predicting creation time from
document content itself is an important task. In this paper, we propose
Attentive Deep Document Dater (AD3), an attention-based neural document dating
system which utilizes both context and temporal information in documents in a
flexible and principled manner. We perform extensive experimentation on
multiple real-world datasets to demonstrate the effectiveness of AD3 over
neural and non-neural baselines.
