文件名称：text_summurization_abstractive_methods：使用Google Colab进行抽象文本摘要的多种实现
文件大小：7.02MB
文件格式：ZIP
更新时间：2024-02-24 12:04:49
nlp machine-learning reinforcement-learning ai deep-learning
文字汇总模型 如果您能够在Arxiv上支持我，我将不胜感激谢谢此回购协议旨在收集用于解决文本摘要的抽象方法的多种实现，语言（印地语，阿姆哈拉语，英语，很快是阿拉伯语） 如果您认为该项目对您有帮助，请考虑引用我们的工作，这对我来说确实意义重大 @INPROCEEDINGS{9068171, author={A. M. {Zaki} and M. I. {Khalil} and H. M. {Abbas}}, booktitle={2019 14th International Conference on Computer Engineering and Systems (ICCES)}
【文件预览】：
text_summurization_abstractive_methods-master
----Implementation A (seq2seq with attention and feature rich representation)()
--------.ipynb_checkpoints()
--------Model_3.ipynb(419KB)
--------README.md(3KB)
--------Model_1.ipynb(94KB)
--------Model 2()
----Implementation C (Reinforcement Learning with seq2seq)()
--------README.md(3KB)
--------Scheduled Sampling with intradecoder()
--------Policy Gradient()
----Arabic()
--------zaksum RL.ipynb(95KB)
--------2_Advanced Cleaning()
--------1_Basic Cleaning()
--------Build Word2Vec.ipynb(49KB)
--------README.md(733B)
----Hindi()
--------3_Build_Word2Vec_VocabDict (google colab).ipynb(35KB)
--------2_process (local).py(2KB)
--------1_NewsCrawler (googel colab).ipynb(32KB)
--------4_Model_5_CL_CSV_py3_Scheduled_Sampling (google colab).ipynb(368KB)
----Implementation B (Pointer Generator seq2seq network)()
--------Results()
--------Model_4_generator_python3.ipynb(8.58MB)
--------Model_4_generator_.ipynb(9.12MB)
--------PreProcessData()
--------README.md(2KB)
--------zaksum_eval.ipynb(13KB)
----Amharic()
--------4_Amhari python3 Model Scheduled Sampling.ipynb(338KB)
--------Readme.md(167B)
--------3_Amhari Build Word2Vec.ipynb(107KB)
--------1_scrap()
--------2_Clean Amhari.ipynb(27KB)
----README.md(8KB)