# ATLAS: Improving Lay Summarisation with Attribute-based Control

arXiv: https://arxiv.org/abs/2406.05625 · HTML: https://mlsanity.com/paper/2406.05625/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts](https://mlsanity.com/paper/1710.06071/) (2017)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://mlsanity.com/paper/2210.09932/) (2022)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [Automated Lay Language Summarization of Biomedical Scientific Reviews](https://mlsanity.com/paper/2012.12573/) (2020)
- [Stylized Story Generation with Style-Guided Planning](https://mlsanity.com/paper/2105.08625/) (2021)
- [Enhancing Biomedical Lay Summarisation with External Knowledge Graphs](https://mlsanity.com/paper/2310.15702/) (2023)
- [A Cross-Attention Augmented Model for Event-Triggered Context-Aware Story Generation](https://mlsanity.com/paper/2311.11271/) (2023)

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

- [Explanatory Summarization with Discourse-Driven Planning](https://mlsanity.com/paper/2504.19339/) (2025)
- [PERCS: Persona-Guided Controllable Biomedical Summarization Dataset](https://mlsanity.com/paper/2512.03340/) (2025)

## Abstract (arXiv)

Lay summarisation aims to produce summaries of scientific articles that are comprehensible to non-expert audiences. However, previous work assumes a one-size-fits-all approach, where the content and style of the produced summary are entirely dependent on the data used to train the model. In practice, audiences with different levels of expertise will have specific needs, impacting what content should appear in a lay summary and how it should be presented. Aiming to address this, we propose ATLAS, a novel abstractive summarisation approach that can control various properties that contribute to the overall "layness" of the generated summary using targeted control attributes. We evaluate ATLAS on a combination of biomedical lay summarisation datasets, where it outperforms state-of-the-art baselines using mainstream summarisation metrics. Additional analyses provided on the discriminatory power and emergent influence of our selected controllable attributes further attest to the effectiveness of our approach.