Unsupervised Abstractive Text Summarization with Length Controlled Autoencoder

Abstract

This work deals with taking an unsupervised approach to abstractive text summarization where a large set of sentences is converted into a concise summary highlighting the essential details. This is achieved with the use of an adversarial autoencoder model. The model encodes the input to a smaller latent vector and the decoder decodes this latent code to generate the higher dimensional output with some loss. Unlike variational autoencoders, AAE's use discriminators to learn using adversarial loss. K-Means clustering and language models are used to get the final summary. This model has been tested with different datasets like the Amazon, Rotten Tomatoes and Yelp reviews dataset to essentially do an opinion summarization task and this is finally evaluated using ROGUE-1, ROGUE-2,ROGUE-L and BLEU scores. The same task is also conducted on a dataset in Hindi. We obtain a ROGUE-1 score of around 24% for Amazon, Yelp and CNN/Daily Mail dataset and a score of 12% for Rotten Tomatoes while the score obtained for the Hindi news articles dataset is only 8%. © 2022 IEEE.