gazalpatel/Text_Summarization_using_Bidirectional_LSTM

Bidirectional LSTM: Abstract Text Summarization

双方向LSTMを用いた文脈を捉える抽象型文章要約

Introduction

Extraction type is an approach of extracting a sentence that seems to be important from sentences to be summarized and creating a summary. The advantages and disadvantages are as follows.

• Pros: Select a sentence in the original sentence to create a summary, so it is less likely to be a summary that is completely out of the way, and it will not be a grammatically strange summary

• Cons: Because you can not use words that are not in the sentence, you can not use abstractions, paraphrases, or conjunctions to make them easier to read. Because of this, the summary created is a crude impression.

I built seq2seq Bidirectional LSTM for Text Summarization task. Also LSTM with Attention is major method for summarization.

Technical Preferences

Title	Detail
Environment	MacOS Mojave 10.14.3
Language	Python
Library	Kras, scikit-learn, Numpy, matplotlib, Pandas, Seaborn
Dataset	BBC Datasets
Algorithm	Encoder-Decoder LSTM

Refference

• Get To The Point: Summarization with Pointer-Generator Networks
• Bidirectional Attentional Encoder-Decoder Model and Bidirectional Beam Search for Abstractive Summarization
• Taming Recurrent Neural Networks for Better Summarization
• Encoder-Decoder Models for Text Summarization in Keras
• Text Summarization Using Keras Models
• Text Summarization in Python: Extractive vs. Abstractive techniques revisited
• 大自然言語時代のための、文章要約