most citedSalience Allocation as Guidance for Abstractive Summarization

4 citations · 9 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CL2022

Efficient Zero-shot Event Extraction with Context-Definition Alignment

Hongming Zhang, Wenlin Yao, Dong Yu

Event extraction (EE) is the task of identifying interested event mentions from text. Conventional efforts mainly focus on the supervised setting. However, these supervised models…

cs.AI20222 cited

MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure

Yinya Huang, Hongming Zhang, Ruixin Hong +3

In this paper, we propose a comprehensive benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Current explanation datasets often employ…

cs.CL20224 cited

Salience Allocation as Guidance for Abstractive Summarization

Fei Wang, Kaiqiang Song, Hongming Zhang +6

Abstractive summarization models typically learn to capture the salient information from scratch implicitly. Recent literature adds extractive summaries as guidance for abstractive…

cs.CL2022

Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction

Haoyu Wang, Hongming Zhang, Yuqian Deng +3

In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. The first perspective is to extract genuinely based on contextual description…

cs.AI2020

Analogous Process Structure Induction for Sub-event Sequence Prediction

Hongming Zhang, Muhao Chen, Haoyu Wang +2

Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence…

cs.CL2020

"What Are You Trying to Do?" Semantic Typing of Event Processes

Muhao Chen, Hongming Zhang, Haoyu Wang +1

This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing, that, given an event process, attempts to infer free-form type labels describi…