  Event extraction (EE) is crucial to downstream tasks such as new aggregation
and event knowledge graph construction. Most existing EE datasets manually
define fixed event types and design specific schema for each of them, failing
to cover diverse events emerging from the online text. Moreover, news titles,
an important source of event mentions, have not gained enough attention in
current EE research. In this paper, We present Title2Event, a large-scale
sentence-level dataset benchmarking Open Event Extraction without restricting
event types. Title2Event contains more than 42,000 news titles in 34 topics
collected from Chinese web pages. To the best of our knowledge, it is currently
the largest manually-annotated Chinese dataset for open event extraction. We
further conduct experiments on Title2Event with different models and show that
the characteristics of titles make it challenging for event extraction,
addressing the significance of advanced study on this problem. The dataset and
baseline codes are available at https://open-event-hub.github.io/title2event.
