Neural sequence-to-sequence models have been successfully applied to text compression. However, these models were trained on huge automatically induced parallel corpora, which are only available for a few domains and tasks. In this paper, we propose a novel interactive setup to neural text compression that enables transferring a model to new domains and compression tasks with minimal human supervision. This is achieved by employing active learning, which intelligently samples from a large pool of unlabeled data. Using this setup, we can successfully adapt a model trained on small data of 40k samples for a headline generation task to a general text compression dataset at an acceptable compression quality with just 500 sampled instances annotated by a human.

In this paper, we propose an extractive multi-document summarization (MDS) system using joint optimization and active learning for content selection grounded in user feedback. Our method interactively obtains user feedback to gradually improve the results of a state-of-the-art integer linear programming (ILP) framework for MDS. Our methods complement fully automatic methods in producing high-quality summaries with a minimum number of iterations and feedbacks. We conduct multiple simulation-based experiments and analyze the effect of feedback-based concept selection in the ILP setup in order to maximize the user-desired content in the summary.