pytorch implementation and enforcement of pointer-generator network

Document Encoder + Pointer Generator to get more key information from the source document.

Model

Performance

How to run training:

1. Follow data generation instruction from https://github.com/abisee/cnn-dailymail
2. Run start_train.sh, you might need to change some path and parameters in data_util/config.py
3. For training run start_train.sh, for decoding run start_decode.sh, and for evaluating run run_eval.sh

Note:

• In decode mode beam search batch should have only one example replicated to batch size https://github.com/atulkum/pointer_summarizer/blob/master/training_ptr_gen/decode.py#L109 https://github.com/atulkum/pointer_summarizer/blob/master/data_util/batcher.py#L226

• It is tested on pytorch 0.4 with python 2.7

• You need to setup pyrouge to get the rouge score

Reference

[1]See A, Liu P J, Manning C D. Get To The Point: Summarization with Pointer-Generator Networks[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017: 1073-1083.

[2]Chung T L, Xu B, Liu Y, et al. Main Point Generator: Summarizing with a Focus[C]//International Conference on Database Systems for Advanced Applications. Springer, Cham, 2018: 924-932.

[3] pytorch implementation of Get To The Point: Summarization with Pointer-Generator Networks, https://github.com/atulkum/pointer_summarizer