Papers by Travis Goodwin

2 papers
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive Summarization (2020.coling-main)

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Challenge: Recent work has shown that pre-trained transformers obtain remarkable performance on many natural language processing tasks including automatic summarization.
Approach: They propose to use transformers to generate multi-document summarization where the summary is explicitly conditioned on a user-given topic statement or question.
Outcome: The proposed models perform well on four challenging summarization datasets from the general domain and one from consumer health.
Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning (2020.findings-emnlp)

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Challenge: Existing methods for automatic summarization are limited to providing general-purpose summaries . ad-hoc nature of the task can cause arbitrary summarizing, causing a problem .
Approach: They propose to use multi-task fine-tuning to enable conditional summarization on five tasks . they propose to combine two novel "online" or adaptive task-mixing strategies .
Outcome: The proposed method improves zero-shot conditional summarization quality on five tasks.

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