Hierarchical Heterogeneous Graph Attention Network for Syntax-Aware Summarization
Authors: Zixing Song, Irwin King11340-11348
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our model is effective for both the abstractive and extractive summarization tasks on five benchmark datasets from various domains. |
| Researcher Affiliation | Academia | Zixing Song, Irwin King The Chinese University of Hong Kong {zxsong, king}@cse.cuhk.edu.hk |
| Pseudocode | No | The paper does not include a figure, block, or section labeled "Pseudocode" or "Algorithm", nor structured steps formatted like pseudocode. |
| Open Source Code | No | The paper provides a link to a constituency parser (https://github.com/Khalil Mrini/LAL-Parser) which is a tool used in their work, but there is no explicit statement or link indicating that the source code for their proposed model (Synap Sum) is available. |
| Open Datasets | Yes | We choose five datasets to evaluate our model. The data split is described in Table 2. CNN/DM (Hermann et al. 2015; See, Liu, and Manning 2017)... New York Times (NYT) (Sandhaus 2008)... Reddit (Kim, Kim, and Kim 2019)... Wiki How (Koupaee and Wang 2018)... Pub Med dataset... Table 2: Dataset Split Avg. Len #Ext Train Valid Test Doc. Sum. CNN/DM 287K 13K 11K 766.1 58.2 3 |
| Dataset Splits | Yes | We choose five datasets to evaluate our model. The data split is described in Table 2. CNN/DM (Hermann et al. 2015; See, Liu, and Manning 2017)... New York Times (NYT) (Sandhaus 2008)... Reddit (Kim, Kim, and Kim 2019)... Wiki How (Koupaee and Wang 2018)... Pub Med dataset... Table 2: Dataset Split Avg. Len #Ext Train Valid Test Doc. Sum. CNN/DM 287K 13K 11K 766.1 58.2 3 |
| Hardware Specification | No | The paper does not specify the hardware used to run the experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions using "Adam optimizer" and a "state-of-the-art constituency parser", but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We choose the Adam optimizer with an initial learning rate 0.0001, momentum values β1 = 0.9, β2 = 0.999 and weight decay ϵ = 10 5. We feed the graph into our model in a mini-batch fashion with a size of 256. In addition, during the decoding step, a beam search strategy is utilized with the beam size of 3. |