# LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation

arXiv: https://arxiv.org/abs/2209.08698 · HTML: https://mlsanity.com/paper/2209.08698/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [LinkBERT: Pretraining Language Models with Document Links](https://mlsanity.com/paper/2203.15827/) (2022)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature](https://mlsanity.com/paper/1806.04185/) (2018)
- [PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization](https://mlsanity.com/paper/2110.08499/) (2021)
- [Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization](https://mlsanity.com/paper/2008.11293/) (2020)

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

- [Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations](https://mlsanity.com/paper/2305.13693/) (2023)
- [PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization](https://mlsanity.com/paper/2311.09836/) (2023)

## Abstract (arXiv)

In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA (Xiao et al., 2022) to the biomedical domain by placing global attention on important biomedical entities in several ways. We analyse the outputs of the 23 resulting models, and report patterns in the results related to the presence of additional global attention, number of training steps, and the input configuration.