Abstractive Text Summarizer Using Attention Machanism
1st Ashwini N. More
Department of Computer Engineering
Shrama Sadhana Bombay Trust’s College Of Engineering and Technology
Jalgaon, India.
ashwinimore490@gmail.com
3rd Yash R. Mahajan
Department of Computer Engineering
Shrama Sadhana Bombay Trust’s College Of Engineering and Technology
Jalgaon, India.
yashmahajan3171@gmail.com
2nd Sakshi P. Mahajan
Department of Computer Engineering
Shrama Sadhana Bombay Trust’s College Of Engineering and Technology
Jalgaon, India.
sakshim325@gmail.com
4th Yashodhan Patil
Department of Computer Engineering
Shrama Sadhana Bombay Trust’s College Of Engineering and Technology
Jalgaon, India.
yashodhanapatil@gmail.com
Abstract— Abstractive text summarization is the task of generating a headline or a short summary consisting of a few sentences that captures the salient ideas of an article or a passage. We use the adjective ‘abstractive’ to denote a summary that is not a mere selection of a few existing passages or sentences extracted from the source.
In this work, we will create the model of abstractive text summarization using Attentional Encoder Decoder Recurrent Neural Networks. Neural sequence-to-sequence models have provided a viable innovative approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we will used standard Long Short-Term Memory (LSTM) sequence-to-sequence attentional model. This method utilizes a local attention model for generating each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. We will apply our model to the Amazon-fine-food-review dataset. We will evaluate the reconstructed paragraph using standard metrics like ROUGE, showing that neural models can encode texts in a way that preserve syntactic, semantic, and discourse coherence.
We propose several features that address problems which are coming in existing system in summarization that data visualization , Audio input, etc. and emitting words that are rare or unseen at training time.
Keywords—component, formatting, style, styling, insert
