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

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset