Movatterモバイル変換


[0]ホーム

URL:


Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

Pytorch implementation of "A Deep Reinforced Model for Abstractive Summarization" paper and pointer generator network

NotificationsYou must be signed in to change notification settings

rohithreddy024/Text-Summarizer-Pytorch

Repository files navigation

CombiningA Deep Reinforced Model for Abstractive Summarization andGet To The Point: Summarization with Pointer-Generator Networks

Model Description

  • LSTM based Sequence-to-Sequence model for Abstractive Summarization
  • Pointer mechanism for handling Out of Vocabulary (OOV) wordsSee et al. (2017)
  • Intra-temporal and Intra-decoder attention for handling repeated wordsPaulus et al. (2018)
  • Self-critic policy gradient training along with MLE trainingPaulus et al. (2018)

Prerequisites

  • Pytorch
  • Tensorflow
  • Python 2 & 3
  • rouge

Data

  • Download train and valid pairs (article, title) of OpenNMT provided Gigaword dataset fromhere
  • Copy filestrain.article.txt,train.title.txt,valid.article.filter.txtandvalid.title.filter.txt todata/unfinished folder
  • Files are already preprcessed

Creating.bin files and vocab file

  • The model accepts data in the form of.bin files.
  • To convert.txt file into.bin file and chunk them further, run (requires Python 2 & Tensorflow):
python make_data_files.py
  • You will find the data indata/chunked folder and vocab file indata folder

Training

python train.py --train_mle=yes --train_rl=no --mle_weight=1.0
  • Next, find the best saved model on validation data by running (with Python 3):
python eval.py --task=validate --start_from=0005000.tar
  • After finding the best model (lets say0100000.tar) with high rouge-l f score, load it and run (with Python 3):
python train.py --train_mle=yes --train_rl=yes --mle_weight=0.25 --load_model=0100000.tar --new_lr=0.0001

for MLE + RL training (or)

python train.py --train_mle=no --train_rl=yes --mle_weight=0.0 --load_model=0100000.tar --new_lr=0.0001

for RL training

Validation

  • To perform validation of RL training, run (with Python 3):
python eval.py --task=validate --start_from=0100000.tar

Testing

  • After finding the best model of RL training (lets say0200000.tar), evaluate it on test data & get all rouge metrics by running (with Python 3):
python eval.py --task=test --load_model=0200000.tar

Results

  • Rouge scores obtained by using best MLE trained model on test set:
    scores: {
    'rouge-1': {'f': 0.4412018559893622, 'p': 0.4814799494024485, 'r': 0.4232331027817015},
    'rouge-2': {'f': 0.23238981595683728, 'p': 0.2531296070596062, 'r': 0.22407861554997008},
    'rouge-l': {'f': 0.40477682528278364, 'p': 0.4584684491434479, 'r': 0.40351107200202596}
    }

  • Rouge scores obtained by using best MLE + RL trained model on test set:
    scores: {
    'rouge-1': {'f': 0.4499047033247696, 'p': 0.4853756369556345, 'r': 0.43544461386607497},
    'rouge-2': {'f': 0.24037014314625643, 'p': 0.25903387205387235, 'r': 0.23362662645146298},
    'rouge-l': {'f': 0.41320241732946406, 'p': 0.4616655167980162, 'r': 0.4144419466382236}
    }

  • Training log file is included in the repository

Examples

article: russia 's lower house of parliament was scheduled friday to debate an appeal to the prime minister that challenged the right of u.s.-funded radio liberty to operate in russia following its introduction of broadcasts targeting chechnya .
ref: russia 's lower house of parliament mulls challenge to radio liberty
dec: russian parliament to debate on banning radio liberty

article: continued dialogue with the democratic people 's republic of korea is important although australia 's plan to open its embassy in pyongyang has been shelved because of the crisis over the dprk 's nuclear weapons program , australian foreign minister alexander downer said on friday .
ref: dialogue with dprk important says australian foreign minister
dec: australian fm says dialogue with dprk important

article: water levels in the zambezi river are rising due to heavy rains in its catchment area , prompting zimbabwe 's civil protection unit -lrb- cpu -rrb- to issue a flood alert for people living in the zambezi valley , the herald reported on friday .
ref: floods loom in zambezi valley
dec: water levels rising in zambezi river

article: tens of thousands of people have fled samarra , about ## miles north of baghdad , in recent weeks , expecting a showdown between u.s. troops and heavily armed groups within the city , according to u.s. and iraqi sources .
ref: thousands flee samarra fearing battle
dec: tens of thousands flee samarra expecting showdown with u.s. troops

article: the #### tung blossom festival will kick off saturday with a fun-filled ceremony at the west lake resort in the northern taiwan county of miaoli , a hakka stronghold , the council of hakka affairs -lrb- cha -rrb- announced tuesday .
ref: #### tung blossom festival to kick off saturday
dec: #### tung blossom festival to kick off in miaoli

References

About

Pytorch implementation of "A Deep Reinforced Model for Abstractive Summarization" paper and pointer generator network

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages


[8]ページ先頭

©2009-2025 Movatter.jp