Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation

30 Jul 2019Yang GaoChristian M. MeyerMohsen MesgarIryna Gurevych

Document summarisation can be formulated as a sequential decision-making problem, which can be solved by Reinforcement Learning (RL) algorithms. The predominant RL paradigm for summarisation learns a cross-input policy, which requires considerable time, data and parameter tuning due to the huge search spaces and the delayed rewards... (read more)

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