Papers by Dongqi Pu
ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer (2023.acl-srw)
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| Challenge: | Large-scale language models, such as ChatGPT, have garnered significant media attention and stunned the public with their remarkable capacity for generating coherent text from short natural language prompts. |
| Approach: | They conduct a systematic inspection of ChatGPT’s performance in two controllable generation tasks and evaluate the faithfulness of the generated text. |
| Outcome: | The proposed model can adapt output to different target audiences and writing styles, and can generate coherent text with human-authored texts. |
Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization (2023.acl-long)
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| Challenge: | Contemporary leading-edge systems for abstractive (long) text summarization employ Transformer encoderdecoder architectures that only consider the nuclearity annotation . |
| Approach: | They propose to incorporate Rhetorical Structure Theory into a novel summarization model that incorporates both the types and uncertainty of rhetorical relations. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation. |
SciNews: From Scholarly Complexities to Public Narratives – a Dataset for Scientific News Report Generation (2024.lrec-main)
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| Challenge: | Scientific news reports are a bridge between academic and scientific publications . however, the pursuit of automated news reports faces challenges due to the insufficient availability of parallel corpora. |
| Approach: | They propose to use a corpus of scientific news reports to facilitate this paradigm development . they highlight the divergences in readability and brevity between scientific news narratives and academic manuscripts . |
| Outcome: | The proposed corpus includes academic publications and scientific news reports across nine disciplines. |
RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization (2024.naacl-long)
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| Challenge: | Existing methods to integrate rhetorical structure theory into long document summarization models are unexplored. |
| Approach: | They propose to integrate rhetorical structure theory into a long document summarization model by explicitly incorporating rhetorical uncertainty into the model. |
| Outcome: | The proposed models outperform the vanilla LoRA and full-parameter fine-tuning models and outperformed previous state-of-the-art methods. |