Many text generation tasks are copy-oriented. For instance, nearly 30% content of news summaries is copied. The copy rate is even higher in Grammatical Error Correction (GEC). However, existing generative models generate texts through word-by-word decoding, which may lead to factual inconsistencies and slow inference. While Elementary Discourse Units (EDUs) are outstanding extraction units, EDU-based extractive methods can alleviate the aforementioned problems. As a consequence, we propose EDUCopy, a framework that integrates the behavior of copying EDUs into generative models. The main idea of EDUCopy is to use special index tags to represent the copied EDUs during generation. Specifically, we extract important EDUs from input sequences, finetune generative models to generate sequences with special index tags, and restore the generated special index tags into corresponding text spans. By doing so, EDUCopy reduces the number of generated tokens significantly. To verify the effectiveness of EDUCopy, we conduct experiments on the news summarization datasets CNNDM, NYT and the GEC datasets FCE, WI-LOCNESS. While achieving notable ROUGE and M2 scores, GPT-4 evaluation validates the strength of our models in terms of factual consistency, fluency, and overall performance. Moreover, compared to baseline models, EDUCopy achieves a significant acceleration of 1.65x.

Query-Focused Summarization (QFS) aims to generate summaries from source documents that can answer specific queries. Although the QFS task has gained increasing attention recently, its development is constrained by the fact that mainstream QFS models are BART variants, which are autoregressive and suffer from long-term dependencies and exposure bias. To address these problems, we adopt a diffusion language model that performs well in non-autoregressive scenarios to effectively resolve issues related to autoregressive methods. However, QFS requires guidance from queries to generate adequate summaries, while diffusion language models have limited sensitivity to queries. In this paper, we propose QFS-DLM, a non-autoregressive diffusion language model that incorporates query-document fragment relevance and query-document global relevance to enhance the adaptability of QFS tasks. Firstly, we extract key fragments from documents based on queries and assign higher weights to them, thereby emphasizing crucial and continuous information within the document. Secondly, we calculate global relevance scores between queries and documents, and then integrate these scores into the model’s loss function, enabling the model to prefer high-quality data and distance itself from low-quality data. Overall, our method achieves state-of-the-art performance on Debatepedia and PubMedQA datasets in ROUGE scores, GPT-4, and human evaluations.