A Reinforcement Learning Approach for Adaptive Single- and Multi-Document Summarization

By Stefan Henß, Margot Mieskes, Iryna Gurevych. 2015

23 citations recorded by the source corpus.

Subfields: Natural Language Processing, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

In Reinforcement Learning

Citations in the corpus

Learning to Extract Coherent Summary via Deep Reinforcement Learning (2018, background): “Although (Ryang and Abekawa 2012; Rioux, Hasan, and Chali 2014; Henb, Mieskes, and Gurevych 2015) use value-based RL algorithms for extractive summarization, all of them are based on handcrafted features and do not consider coherence as the part of the reward.”
Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018, methodology): “Follow-on work (Rioux et al., 2014) extends this approach by employing ROUGE as part of the reward function, while Henß et al. (2015) further experiment with Q -learning.”
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting (2018, methodology): “Henß et al. (2015) use Q-learning based RL for extractive summarization.”
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (2018, background): “As compared to extractive or compressive summarization (Jing and McKeown, 2000; Knight and Marcu, 2002; Clarke and Lapata, 2008; Filippova et al., 2015; Henß et al., 2015), abstractive summaries are based on rewriting as opposed to selecting.”
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning (2018, methodology): “Henß et al. (2015) proposed a different RL formulation of EMDS and jointly used supervised learning and RL to perform the task.”
An Overview of Natural Language State Representation for Reinforcement Learning (2020, methodology): “Initial works applying RL used tuples of features to represent the state, composed of the summary at each time step, a history of actions and a binary variable indicating the terminal state (Ryang & Abekawa, 2012; Rioux et al., 2014; Henß et al., 2015).”
Preference-based interactive multi-document summarisation (2019, methodology): “RL has been applied to both extractive and abstrac-tive summarisation in recent years (Ryang and Abekawa 2012; Rioux et al. 2014; Gkatzia et al. 2014; Henß et al. 2015; Paulus et al. 2017; Pasunuru and Bansal 2018; Kryscinski et al. 2018).”