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20182025
most citedTowards Improving Faithfulness in Abstractive Summarization

14 citations · 42 across the 11 of their papers we have counts for

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cs.CL2025

CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis

Ruixiang Feng, Shen Gao, Xiuying Chen +2

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they often exhibit a specific cultural biases, neglecting the values and linguistic…

cs.CL2025

Stick to Facts: Towards Fidelity-oriented Product Description Generation

Zhangming Chan, Xiuying Chen, Yongliang Wang +5

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute infor…

cs.CL2024

Write Summary Step-by-Step: A Pilot Study of Stepwise Summarization

Xiuying Chen, Shen Gao, Mingzhe Li +3

Nowadays, neural text generation has made tremendous progress in abstractive summarization tasks. However, most of the existing summarization models take in the whole document all…

cs.CL2024

Rethinking Scientific Summarization Evaluation: Grounding Explainable Metrics on Facet-aware Benchmark

Xiuying Chen, Tairan Wang, Qingqing Zhu +5

The summarization capabilities of pretrained and large language models (LLMs) have been widely validated in general areas, but their use in scientific corpus, which involves comple…

cs.CL2022

Scientific Paper Extractive Summarization Enhanced by Citation Graphs

Xiuying Chen, Mingzhe Li, Shen Gao +3

In a citation graph, adjacent paper nodes share related scientific terms and topics. The graph thus conveys unique structure information of document-level relatedness that can be u…

cs.CL202214 cited

Towards Improving Faithfulness in Abstractive Summarization

Xiuying Chen, Mingzhe Li, Xin Gao +1

Despite the success achieved in neural abstractive summarization based on pre-trained language models, one unresolved issue is that the generated summaries are not always faithful…