Combination of abstractive and extractive approaches for summarization of long scientific texts
This addresses the challenge of generating concise summaries for long scientific documents, though it is incremental as it builds on existing transformer-based methods.
The authors tackled the problem of summarizing long scientific texts by combining extractive and abstractive approaches, resulting in significantly improved ROUGE scores.
In this research work, we present a method to generate summaries of long scientific documents that uses the advantages of both extractive and abstractive approaches. Before producing a summary in an abstractive manner, we perform the extractive step, which then is used for conditioning the abstractor module. We used pre-trained transformer-based language models, for both extractor and abstractor. Our experiments showed that using extractive and abstractive models jointly significantly improves summarization results and ROUGE scores.