Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization
Authors: Abhishek Singh, Manish Gupta, Vasudeva Varma
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC2004) indicate that our model shows significant performance gain of around 1.5-2 points in terms of ROUGE score compared with the state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | Abhishek Kumar Singh, Manish Gupta, Vasudeva Varma IIIT Hyderabad, India abhishek.singh@research.iiit.ac.in, manish.gupta@iiit.ac.in, vv@iiit.ac.in. The author is also a Principal Applied Researcher at Microsoft. |
| Pseudocode | No | The paper describes its methods using prose, equations, and diagrams, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | Initial training of our model is done on the Daily Mail corpus, used for the task of single document summarization by (Cheng and Lapata 2016)... We experiment on DUC 2001-2004 datasets... The full DUC data set can be availed by request at http://duc.nist.gov/data.html. The SICK dataset which contains 9927 sentence pairs with a 5,000/4,927 training/test split (Marelli et al. 2014) was used for training the Siam CSTI net. |
| Dataset Splits | Yes | Overall, we have 193986 training documents, 12147 validation documents and 10350 test documents in the corpus... DUC 2003 data is used as development set and we perform a 3-fold cross-validation on DUC 2001, 2002 and 2004 datasets with two years of data as training set and one year of data as the test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions tools like 'Adam' and 'LIBLINEAR' but does not specify their version numbers or other software dependencies with version information required for reproducibility. |
| Experiment Setup | Yes | The size of the hidden units of BLSTM was set to 150. After tuning on the validation set, we fix the dimension m of the latent features from convolutional encoder as 125 and window size k = 5 for HNet system. We use Adam (Kingma and Ba 2014) as the optimizer with mini batches of size 35. Learning rates are set to {0.009, 0.0009}. For our network, we use regularization dropout of {0.2, 0.5}. |