Extractive approach for text summarisation using graphs
This is an incremental study for NLP researchers, focusing on improving extractive text summarization methods.
The paper tackled the text summarization problem by exploring graph-related algorithms with an extractive approach, using sentence overlap and edit distance for similarity measurement, but no concrete results or numbers were reported.
Natural language processing is an important discipline with the aim of understanding text by its digital representation, that due to the diverse way we write and speak, is often not accurate enough. Our paper explores different graph-related algorithms that can be used in solving the text summarization problem using an extractive approach. We consider two metrics: sentence overlap and edit distance for measuring sentence similarity.