Improving Neural Text Summarization using Knowledge Graphs
In this paper, we propose a method for extractive text summarization using auto-regressive transformers. For better learning procedure we adopt the knowledge graph method to convert our textual data to more informative text and unsupervised training methods for wide use. We feed the informative text to our pre-trained generative model to summarize the text more properly and infer on generating a proper summary. The model is able to summarize input text into adequate information and is capable of performing several Natural Language Processing(NLP) tasks.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
