Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering

Avi Caciularu, Matthew Peters, Jacob Goldberger, Ido Dagan, Arman Cohan

Main: Summarization Main-poster Paper

Poster Session 1: Summarization (Poster)
Conference Room: Frontenac Ballroom and Queen's Quay
Conference Time: July 10, 11:00-12:30 (EDT) (America/Toronto)
Global Time: July 10, Poster Session 1 (15:00-16:30 UTC)
Keywords: query-focused summarization, multi-document summarization
TLDR: The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose extending this idea by pre-training a generic multi-document model from a novel cross-document question answering pre-t...
You can open the #paper-P2201 channel in a separate window.
Abstract: The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose extending this idea by pre-training a generic multi-document model from a novel cross-document question answering pre-training objective. To that end, given a set (or cluster) of topically-related documents, we systematically generate semantically-oriented questions from a salient sentence in one document and challenge the model, during pre-training, to answer these questions while "peeking" into other topically-related documents. In a similar manner, the model is also challenged to recover the sentence from which the question was generated, again while leveraging cross-document information. This novel multi-document QA formulation directs the model to better recover cross-text informational relations, and introduces a natural augmentation that artificially increases the pre-training data. Further, unlike prior multi-document models that focus on either classification or summarization tasks, our pre-training objective formulation enables the model to perform tasks that involve both short text generation (e.g., QA) and long text generation (e.g., summarization). Following this scheme, we pre-train our model - termed QAmden - and evaluate its performance across several multi-document tasks, including multi-document QA, summarization, and query-focused summarization, yielding improvements of up to 7\%, and significantly outperforms zero-shot GPT-3.5 and GPT-4.