# Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization

arXiv: https://arxiv.org/abs/2204.00290 · HTML: https://mlsanity.com/paper/2204.00290/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [Faithful to the Original: Fact Aware Neural Abstractive Summarization](https://mlsanity.com/paper/1711.04434/) (2017)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [Inferring Which Medical Treatments Work from Reports of Clinical Trials](https://mlsanity.com/paper/1904.01606/) (2019)
- [Text Summarization Techniques: A Brief Survey](https://mlsanity.com/paper/1707.02268/) (2017)
- [Multi-document Summarization via Deep Learning Techniques: A Survey](https://mlsanity.com/paper/2011.04843/) (2020)
- [Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization](https://mlsanity.com/paper/2008.11293/) (2020)
- [Evidence Inference 2.0: More Data, Better Models](https://mlsanity.com/paper/2005.04177/) (2020)
- [Overview of BioASQ 2021: The ninth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering](https://mlsanity.com/paper/2106.14885/) (2021)
- [Predicting Clinical Trial Results by Implicit Evidence Integration](https://mlsanity.com/paper/2010.05639/) (2020)

## Abstract (arXiv)

Clinical trials offer a fundamental opportunity to discover new treatments and advance the medical knowledge. However, the uncertainty of the outcome of a trial can lead to unforeseen costs and setbacks. In this study, we propose a new method to predict the effectiveness of an intervention in a clinical trial. Our method relies on generating an informative summary from multiple documents available in the literature about the intervention under study. Specifically, our method first gathers all the abstracts of PubMed articles related to the intervention. Then, an evidence sentence, which conveys information about the effectiveness of the intervention, is extracted automatically from each abstract. Based on the set of evidence sentences extracted from the abstracts, a short summary about the intervention is constructed. Finally, the produced summaries are used to train a BERT-based classifier, in order to infer the effectiveness of an intervention. To evaluate our proposed method, we introduce a new dataset which is a collection of clinical trials together with their associated PubMed articles. Our experiments, demonstrate the effectiveness of producing short informative summaries and using them to predict the effectiveness of an intervention.