Literaturnachweis - Detailanzeige
| Autor/inn/en | Magooda, Ahmed; Elaraby, Mohamed; Litman, Diane |
|---|---|
| Titel | Exploring Multitask Learning for Low-Resource Abstractive Summarization |
| Quelle | (2021), (10 Seiten)
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| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Data Analysis; Synthesis; Documentation; Training; Classification; Sampling; Models; Prediction; Transfer of Training |
| Abstract | This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstraction summarization via multitask learning. We show that for many task combinations, a model trained in a multitask setting outperforms a model trained only for abstractive summarization, with no additional summarization data introduced. Additionally, we do a comprehensive search and find that certain tasks (e.g. paraphrase detection) consistently benefit abstractive summarization, not only when combined with other tasks but also when using different architectures and training corpora. [This paper was published in: "Findings of the Association for Computational Linguistics: EMNLP 2021," Association for Computational Linguistics, 2021, pp. 1652-1661.] (As Provided). |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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