Objective Function Learning to Match Human Judgements for Optimization-Based Summarization
Supervised summarization systems usually rely on supervision at the sentence or n-gram level provided by automatic metrics like ROUGE, which act as noisy proxies for human judgments. In this work, we learn a summary-level scoring function $\theta$ including human judgments as supervision and automatically generated data as regularization. We extract summaries with a genetic algorithm using $\theta$ as a fitness function. We observe strong and promising performances across datasets in both automatic and manual evaluation.
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