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

• URL: http://arxiv.org/abs/2305.15387v1
• Date: Wed, 24 May 2023 17:48:40 GMT
• Title: Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
• Authors: Avi Caciularu, Matthew E. Peters, Jacob Goldberger, Ido Dagan, Arman Cohan
• Abstract summary: We pre-training a generic multi-document model from a novel cross-document question answering pre-training objective. This novel multi-document QA formulation directs the model to better recover cross-text informational relations. 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 and long text generation.
• Score: 49.85790367128085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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