# Word Graph Guided Summarization for Radiology Findings

arXiv: https://arxiv.org/abs/2112.09925 · HTML: https://mlsanity.com/paper/2112.09925/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 18 Dec 2021.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://mlsanity.com/paper/1909.03186/) (2019)
- [Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward](https://mlsanity.com/paper/2005.01159/) (2020)
- [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018)
- [A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss](https://mlsanity.com/paper/1805.06266/) (2018)
- [Ontology-Aware Clinical Abstractive Summarization](https://mlsanity.com/paper/1905.05818/) (2019)
- [Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization](https://mlsanity.com/paper/2005.00163/) (2020)
- [ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders](https://mlsanity.com/paper/2105.01279/) (2021)
- [Controlling the Amount of Verbatim Copying in Abstractive Summarization](https://mlsanity.com/paper/1911.10390/) (2019)
- [Global Encoding for Abstractive Summarization](https://mlsanity.com/paper/1805.03989/) (2018)
- [SRLGRN: Semantic Role Labeling Graph Reasoning Network](https://mlsanity.com/paper/2010.03604/) (2020)
- [Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks](https://mlsanity.com/paper/2010.06115/) (2020)
- [Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation](https://mlsanity.com/paper/2010.13389/) (2020)
- [Exploring Explainable Selection to Control Abstractive Summarization](https://mlsanity.com/paper/2004.11779/) (2020)

## Built on by (most cited first, 19 of 19)

- [An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT](https://mlsanity.com/paper/2304.08448/) (2023)
- [Radiology-GPT: A Large Language Model for Radiology](https://mlsanity.com/paper/2306.08666/) (2023)
- [Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts](https://mlsanity.com/paper/2302.08958/) (2023)
- [PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation](https://mlsanity.com/paper/2407.05721/) (2024)
- [Graph Enhanced Contrastive Learning for Radiology Findings Summarization](https://mlsanity.com/paper/2204.00203/) (2022)
- [Toward expanding the scope of radiology report summarization to multiple anatomies and modalities](https://mlsanity.com/paper/2211.08584/) (2022)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction](https://mlsanity.com/paper/2205.09536/) (2022)
- [FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization](https://mlsanity.com/paper/2303.08335/) (2023)
- [Improving Radiology Summarization with Radiograph and Anatomy Prompts](https://mlsanity.com/paper/2210.08303/) (2022)
- [Automatic Personalized Impression Generation for PET Reports Using Large Language Models](https://mlsanity.com/paper/2309.10066/) (2023)
- [KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization](https://mlsanity.com/paper/2307.07409/) (2023)
- [Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis](https://mlsanity.com/paper/2307.12803/) (2023)
- [Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports](https://mlsanity.com/paper/2309.00917/) (2023)
- [Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity](https://mlsanity.com/paper/2410.00448/) (2024)
- [CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization](https://mlsanity.com/paper/2503.05750/) (2025)
- [Learning Semi-Structured Representations of Radiology Reports](https://mlsanity.com/paper/2112.10746/) (2021)
- [RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion](https://mlsanity.com/paper/2210.08242/) (2022)
- [A Second Look on BASS -- Boosting Abstractive Summarization with Unified Semantic Graphs -- A Replication Study](https://mlsanity.com/paper/2403.02930/) (2024)

## Abstract (arXiv)

Radiology reports play a critical role in communicating medical findings to physicians. In each report, the impression section summarizes essential radiology findings. In clinical practice, writing impression is highly demanded yet time-consuming and prone to errors for radiologists. Therefore, automatic impression generation has emerged as an attractive research direction to facilitate such clinical practice. Existing studies mainly focused on introducing salient word information to the general text summarization framework to guide the selection of the key content in radiology findings. However, for this task, a model needs not only capture the important words in findings but also accurately describe their relations so as to generate high-quality impressions. In this paper, we propose a novel method for automatic impression generation, where a word graph is constructed from the findings to record the critical words and their relations, then a Word Graph guided Summarization model (WGSum) is designed to generate impressions with the help of the word graph. Experimental results on two datasets, OpenI and MIMIC-CXR, confirm the validity and effectiveness of our proposed approach, where the state-of-the-art results are achieved on both datasets. Further experiments are also conducted to analyze the impact of different graph designs to the performance of our method.