@inproceedings{peyrard-gurevych-2018-objective,
    title = "Objective Function Learning to Match Human Judgements for Optimization-Based Summarization",
    author = "Peyrard, Maxime  and
      Gurevych, Iryna",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/N18-2103/",
    doi = "10.18653/v1/N18-2103",
    pages = "654--660",
    abstract = "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."
}