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Mengna Zhu

4 accepted papers

2025

Can Large Language Models Tackle Graph Partitioning?

EMNLP 2025

Large language models (LLMs) demonstrate remarkable capabilities in understanding complex tasks and have achieved commendable performance in graph-related tasks, such as node classification, link prediction, and subgraph classification. These tasks primarily depend on the local reasoning capabilitie

Cited by 0SourcePDFScholar
2025

EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents

AAAI 2025technical

In real life, many dynamic events, such as major disasters and large-scale sports events, evolve continuously over time. Obtaining an overview of these events can help people quickly understand the situation and respond more effectively. This is challenging because the key information of the event i…

2024

CMNEE:A Large-Scale Document-Level Event Extraction Dataset Based on Open-Source Chinese Military News

COLING 2024main

Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes…

2024

LC4EE: LLMs as Good Corrector for Event Extraction

ACL 2024findings

Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. On one hand, powerful large language models (LLMs) currently show poor performance because EE task is more complex than other tasks. On the other hand, state-of-the-art (…