Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation
Authors: Yang Gao, Christian M. Meyer, Mohsen Mesgar, Iryna Gurevych
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we evaluate our approach on extractive multi-document summarisation. We show that RELIS reduces the training time by two orders of magnitude compared to the state-of-the-art models while performing on par with them. |
| Researcher Affiliation | Academia | Yang Gao1 , Christian Meyer2 , Mohsen Mesgar2 and Iryna Gurevych2 1Dept. of Computer Science, Royal Holloway, University of London 2Ubiquitous Knowledge Processing Lab (UKP-TUDA), Technische Universit at Darmstadt |
| Pseudocode | No | The paper describes algorithms and methods but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code and supplementary material are available at https://github.com/UKPLab/ijcai2019-relis. |
| Open Datasets | Yes | We evaluate RELIS for extractive multi-document summarisation on three benchmark datasets from the Document Understanding Conferences (DUC)2 described in Table 1. 2https://duc.nist.gov/ |
| Dataset Splits | Yes | To decide the best parameters, we perform 10-fold cross validation on DUC 01. In each run in the leave-one-out experiments, we randomly select 30% data from the training set as the dev set, and select the model with the best performance on the dev set. |
| Hardware Specification | Yes | We run RELIS, SRSum, Deep TD and REAPER on the same workstation with a 4-core CPU, 8 GB memory and no GPUs. |
| Software Dependencies | No | The paper mentions software like Adam, Infer Sent, and DQN-based RL summariser, but it does not specify version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | We use Adam with initial learning rate 10^-2. The number of epochs is 10 and batch size is 2. |