# Combination of abstractive and extractive approaches for summarization of long scientific texts

arXiv: https://arxiv.org/abs/2006.05354 · HTML: https://mlsanity.com/paper/2006.05354/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [BERT-VBD: Vietnamese Multi-Document Summarization Framework](https://mlsanity.com/paper/2409.12134/) (2024)
- [QFS-Composer: Query-focused summarization pipeline for less resourced languages](https://mlsanity.com/paper/2604.10687/) (2026)

## Abstract (arXiv)

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.