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

arXiv: https://arxiv.org/abs/2211.00869 · HTML: https://mlsanity.com/paper/2211.00869/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [A Survey on Open Information Extraction](https://mlsanity.com/paper/1806.05599/) (2018)
- [Neural Open Information Extraction](https://mlsanity.com/paper/1805.04270/) (2018)
- [EventKG: A Multilingual Event-Centric Temporal Knowledge Graph](https://mlsanity.com/paper/1804.04526/) (2018)
- [One for All: Neural Joint Modeling of Entities and Events](https://mlsanity.com/paper/1812.00195/) (2018)
- [IMoJIE: Iterative Memory-Based Joint Open Information Extraction](https://mlsanity.com/paper/2005.08178/) (2020)
- [Span Model for Open Information Extraction on Accurate Corpus](https://mlsanity.com/paper/1901.10879/) (2019)
- [A Survey on Neural Open Information Extraction: Current Status and Future Directions](https://mlsanity.com/paper/2205.11725/) (2022)
- [Nugget Proposal Networks for Chinese Event Detection](https://mlsanity.com/paper/1805.00249/) (2018)
- [Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering](https://mlsanity.com/paper/2203.07633/) (2022)
- [Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations](https://mlsanity.com/paper/2006.11824/) (2020)
- [Label Enhanced Event Detection with Heterogeneous Graph Attention Networks](https://mlsanity.com/paper/2012.01878/) (2020)

## Built on by (most cited first, 14 of 14)

- [CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting](https://mlsanity.com/paper/2501.08620/) (2025)
- [Topology-aware Reinforcement Feature Space Reconstruction for Graph Data](https://mlsanity.com/paper/2411.05742/) (2024)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection](https://mlsanity.com/paper/2305.14163/) (2023)
- [Event-Centric Query Expansion in Web Search](https://mlsanity.com/paper/2305.19019/) (2023)
- [Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions](https://mlsanity.com/paper/2411.02558/) (2024)
- [Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts](https://mlsanity.com/paper/2504.07459/) (2025)
- [Event-enhanced Retrieval in Real-time Search](https://mlsanity.com/paper/2404.05989/) (2024)
- [Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models](https://mlsanity.com/paper/2410.03521/) (2024)
- [Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis](https://mlsanity.com/paper/2502.03843/) (2025)
- [Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferences](https://mlsanity.com/paper/2404.08666/) (2024)
- [Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement](https://mlsanity.com/paper/2601.19170/) (2026)
- [Schema-Constrained Document-Level Event Argument Extraction with Lightweight LLM Fine-Tuning](https://mlsanity.com/paper/2607.16808/) (2026)
- [BackTrend: Evaluating Scientific Weak-Signal Prediction via Backward Reconstruction](https://mlsanity.com/paper/2609.24921/) (2026)

## Abstract (arXiv)

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.