Create README.md

README.md

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+# Summary-T-ConvS2S
+
+An implementation of [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://arxiv.org/abs/1808.08745)
+
+# XSum Dataset
+
+Follow the steps mentioned in this [README](https://github.com/ramshankar99/Summary-T-ConvS2S/blob/master/dataset/README.md) for
+1. Generating the XSum dataset starting from bbc Urls
+2. Training the LDA Model from scratch 
+3. Decoding word-topics and doc-topics using the LDA model
+4. Data Processing 
+
+# Training 
+
+## Data Preprocessing 
+Generate source and target dictionary files. In this case, both files are identical (due to "--joined-dictionary"). It operates on the raw format data.
+```
+TEXT= {path to xsum_data_topic_convs2s dir}
+!python ./XSum-Topic-ConvS2S/preprocess.py --source-lang document \
+                                         --target-lang summary \
+                                         --trainpref $TEXT/train \
+                                         --validpref $TEXT/validation \
+                                         --testpref $TEXT/test \
+                                         --destdir $TEXT \
+                                         --joined-dictionary \
+                                         --nwordstgt 50000 \
+                                         --nwordssrc 50000 \
+                                         --output-format raw
+```
+
+## Model Training 
+
+The model requires GPU for training. Check usage with -h for changing variant and hyperparameters
+```
+save_directory = "./checkpoints-topic-convs2s"
+CUDA_VISIBLE_DEVICES=1 
+!python ./dataset/scripts/XSum-Topic-ConvS2S/train.py $TEXT --source-lang document \
+                                                            --target-lang summary \
+                                                            --doctopics doc-topics \
+                                                            --max-sentences 32 \
+                                                            --arch fconv \
+                                                            --variant 2 \
+                                                            --criterion label_smoothed_cross_entropy \
+                                                            --max-epoch 200 \
+                                                            --clip-norm 0.1 \
+                                                            --lr 0.10 \
+                                                            --dropout 0.2 \
+                                                            --save-dir {save_directory} \
+                                                            --no-progress-bar \
+                                                            --log-interval 10
+```
+
+
+# ROUGE
+
+```
+!python path/eval_rouge.py --summary {system_summary_file} --mod_sum {model_summary_file}
+```
+Take txt files with generated summaries and a file with the corresponding model gold summaries and evaluates P, R, F on rouge-1, rouge-2, rouge-l
+Sample Output
+```
+rouge-1:	P: 30.00	R: 37.50	F1: 33.33
+rouge-2:	P: 11.11	R: 14.29	F1: 12.50
+rouge-l:	P: 26.15	R: 31.50	F1: 28.58
+```