论文标题
ctrlsum：迈向通用可控文本摘要
CTRLsum: Towards Generic Controllable Text Summarization
论文作者
论文摘要
当前的摘要系统产生的通用摘要与用户的偏好和期望断开。为了解决这一限制，我们提出了Ctrlsum，这是一个可控摘要的新型框架。我们的方法使用户可以通过以一组关键字或描述性提示的形式与摘要系统进行交互，以控制生成的摘要的多个方面。使用单个统一模型，CTRLSUM能够在推理时间内实现广泛的摘要操作范围，而无需其他人类注释或预先定义训练过程中的一组控制方面。我们定量地证明了我们的方法对摘要数据集的三个领域和五个控制方面的有效性：1）以实体为中心和2）长度控制可控制的摘要，3）对科学论文的贡献摘要，4）发明目的摘要对专利档案的目的汇总以及5）关于阅读理解的新闻艺术的问题指导的汇总。此外，当在标准，不受控制的摘要设置中使用时，CTRLSUM在CNN/DailyMail数据集上实现了最先进的结果。代码和模型检查点可在https://github.com/salesforce/ctrl-sum上找到
Current summarization systems yield generic summaries that are disconnected from users' preferences and expectations. To address this limitation, we present CTRLsum, a novel framework for controllable summarization. Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts. Using a single unified model, CTRLsum is able to achieve a broad scope of summary manipulation at inference time without requiring additional human annotations or pre-defining a set of control aspects during training. We quantitatively demonstrate the effectiveness of our approach on three domains of summarization datasets and five control aspects: 1) entity-centric and 2) length-controllable summarization, 3) contribution summarization on scientific papers, 4) invention purpose summarization on patent filings, and 5) question-guided summarization on news articles in a reading comprehension setting. Moreover, when used in a standard, uncontrolled summarization setting, CTRLsum achieves state-of-the-art results on the CNN/DailyMail dataset. Code and model checkpoints are available at https://github.com/salesforce/ctrl-sum
