swaroop-nath/rqft

Tasks

• summarization

Citation text from the card

@inproceedings{nath-etal-2023-reinforcement,
    title = "Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning",
    author = "Nath, Swaroop  and
      Bhattacharyya, Pushpak  and
      Khadilkar, Harshad",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.977",
    doi = "10.18653/v1/2023.emnlp-main.977",
    pages = "15770--15789",
    abstract = "Query-focused Summarization (QfS) deals with systems that generate summaries from document(s) based on a query. Motivated by the insight that Reinforcement Learning (RL) provides a generalization to Supervised Learning (SL) for Natural Language Generation, and thereby performs better (empirically) than SL, we use an RL-based approach for this task of QfS. Additionally, we also resolve the conflict of employing RL in Transformers with Teacher Forcing. We develop multiple Policy Gradient networks, trained on various reward signals: ROUGE, BLEU, and Semantic Similarity, which lead to a $\mathit{10}$-point improvement over the State-of-the-Art approach on the ROUGE-L metric for a benchmark dataset (ELI5). We also show performance of our approach in zero-shot setting for another benchmark

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