This paper incorporates the efficiency of automatic summarization and addresses the challenge of generating personalized summaries tailored to individual users' interests and requirements. To tackle this challenge, we introduce SummPilot, an interaction-based customizable summarization system. SummPilot leverages a large language model to facilitate both automatic and interactive summarization. Users can engage with the system to understand document content and personalize summaries through interactive components such as semantic graphs, entity clustering, and explainable evaluation. Our demo and user studies demonstrate SummPilot's adaptability and usefulness for customizable summarization.

Deep learning models often learn and exploit spurious correlations in training data, using these non-target features to inform their predictions.Such reliance leads to performance degradation and poor generalization on unseen data.To address these limitations, we introduce a more general form of counterfactual data augmentation, termed counterbias data augmentation, which simultaneously tackles multiple biases (e.g., gender bias, simplicity bias) and enhances out-of-distribution robustness.We present COBA: CounterBias Augmentation, a unified framework that operates at the semantic triple level: first decomposing text into subjectpredicate-object triples, then selectively modifying these triples to disrupt spurious correlations.By reconstructing the text from these adjusted triples, COBA generates counterbias data that mitigates spurious patterns.Through extensive experiments, we demonstrate that COBA not only improves downstream task performance, but also effectively reduces biases and strengthens out-of-distribution resilience, offering a versatile and robust solution to the challenges posed by spurious correlations.

Kyeongpil Kang, Kyohoon Jin, Soyoung Yang, Soojin Jang, Jaegul Choo, Youngbin Kim. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.