This is the project for automatically generating summarizatons given NBA game box-score.
- Python 3.6
- PyTorch 0.2
We use Rotowire dataset for training as in Challenges in Data-to-Document Generation (Wiseman, Shieber, Rush; EMNLP 2017). This dataset consists of (human-written) NBA basketball game summaries aligned with their corresponding box- and line-scores.
- First extract the dataset, using
tar -jxvf boxscore-data/rotowire.tar.bz2. - Go to directory train/, using
cd train - (Optional) Train the model, using
python3 train.py. - Generate some text, using
python3 small_evaluate.py
Some pre-trained model files could be found here. Extract them under model/ directory.
- Sample Generate Summary using BiLSTM encoder
The Chicago Bulls defeated the Orlando Magic , 102 - 92 , at
United Center on Saturday . The Bulls ( 10 - 2 ) were expected
to win this game rather easily and they did n’t disappoint in the
fourth quarter . In fact , they outscored the Magic by 12 points in
the fourth quarter to pull out the win . Defense was key for the
Bulls , as they held the Magic to 37 percent from the field and 25
percent from three - point range . The Magic also dominated the
rebounding , winning that battle , 44 - 32 . The Magic ( 8 - 18 )
had to play this game without Joel Embiid and Nikola Vucevic ,
as they simply did n’t have enough to get out of hand . Jimmy
Butler was the player of the game , as he tallied 23 points , five
rebounds and four assists . Aaron Gordon was the only other
starter with more than 10 points , as he totaled 15 points , five
rebounds and four assists. . . ...
The train/train.py accepts the following arguments.
(The default configurations for each argument can be found in train/settings.py.)
# Parameters for model
-embed EMBEDDING_SIZE, the hidden size for embedding,, default = 600
-lr LR, initial learning rate, default = 0.01
-batch BATCH_SIZE, batch size, default = 2
# Parameters for model
-encoder ENCODER_STYLE, type of encoder NN (LIN, BiLSTM, RNN, BiLSTMMax, HierarchicalRNN,
HierarchicalBiLSTM, HierarchicalLIN)
-decoder DECODER_STYLE, type of decoder NN (RNN, HierarchcialRNN)
-copy, if apply pointer-generator network(True, False), default = False
# Parameters for training
-gradclip GRAD_CLIP, gradient clipping, default = 2
-pretrain PRETRAIN, file name of pretrained model (must assign with iternum)
-iternum ITER_NUM, file name of pretraiend model (must assign with pretrain)
-layer LAYER_DEPTH, the depth of recurrent units, default = 2; no depth for linear units
-copyplayer COPY_PLAYER, if include player's information in data, default = False
-epoch EPOCH_TIME, maximum epoch time for training
-maxlength MAX_LENGTH, maximum words for each sentence
-maxsentece MAX_SENTECE, limit the maximum length for training. Set lower (e.g 5) for faster training speed. If not specify, program will train entire corpus.
# Parameters for display
-getloss GET_LOSS, print out average loss every `GET_LOSS` iterations.
-epochsave SAVE_MODEL, save the model every `SAVE_MODEL` epochs.
-outputfile OUTPUT_FILE, starting name for saving the model. During training, encoder and decoder would be saved as `[OUTPUT_FILE]_[encoder|decoder]_[iter_time]` every `SAVE_MODEL` epochs.
With above arguments, a variety of configurations could be trained:
python train.py # This will train using default settings in `train/settings.py`
python train.py -embed 300 -lr 0.01 -batch 3 -getloss 20 -encoder HierarchicalRNN
-decoder HierarchicalRNN -epochsave 12 -copy True -copyplayer False
-gradclip 2 -layer 2 -epoch 3 -outputfile pretrain_copy
-pretrain hbilstm -iternum 200
# -pretrain and -iternum must be specified together
# the corresponding pretrained model name will be in the format:
# [pretrain]_[encoder|decoder|optim]_[iter_num]
python train.py -embed 720 -lr 0.02 -batch 3 -getloss 10 -encoder HierarchicalLIN
-decoder HierarchicalRNN -epochsave 5 -copy True -copyplayer True
-gradclip 3 -maxsentence 800 -epoch 3
python train.py -embed 512 -lr 0.03 -batch 3 -getloss 10 -encoder BiLSTM
-decoder RNN -epochsave 12 -copy True -copyplayer True
-gradclip 3 -maxsentence 230 -layer 2 -epoch 3
python train.py -embed 600 -lr 0.02 -batch 3 -getloss 10 -encoder LIN
-decoder RNN -epochsave 12 -copy True -copyplayer False
-gradclip 5 -maxsentence 800 -layer 2 -epoch 3In train/ directory is the part of data2text generation. The files are for this part include:
- dataprepare.py -- word2index map class, storing the vocabulary and relations
- model.py -- The file that contains the implementation of several encoder, decoder and embedding model class
- preprocessing.py -- mainly for read of parse the data
- train.py -- the file that defines the training processes
- util.py -- utility functions for time, showing etc.
- setting.py -- store the hyper-parameter, file location etc.
In evaluate/ directory is the extraction evaluation system, based on Challenges in Data-to-Document Generation (Wiseman, Shieber, Rush; EMNLP 2017) and part of their codes. The files from their repo contains:
- data_utils.py -- for the data parsing and cleaning
- extractor.lua -- the main script for relation classifier
- non_rg_metrics.py -- CS and RO computation