paper-with-me

Papers

BDKG at MEDIQA 2021: System Report for the Radiology Report Summarization Task

2021-06-01 · NAACL (BioNLP) 2021 6 · Songtai Dai, Quan Wang, Yajuan Lyu, Yong Zhu

This paper presents our winning system at the Radiology Report Summarization track of the MEDIQA 2021 shared task. Radiology report summarization automatically summarizes radiology findings into free-text impressions. This year’s task emphasizes the generalization and transfer ability of participating systems. Our system is built upon a pre-trained Transformer encoder-decoder architecture, i.e., PEGASUS, deployed with an additional domain adaptation module to particularly handle the transfer and generalization issue. Heuristics like ensemble and text normalization are also used. Our system is conceptually simple yet highly effective, achieving a ROUGE-2 score of 0.436 on test set and ranked the 1st place among all participating systems.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

DecoderDomain AdaptationText Normalization

Methods 이 논문이 사용한 방법론

PEGASUS PEGASUS proposes a transformer-based model for abstractive summarization. It uses a special self-supervised pre-training objective called gap-sentences generation (GSG) that's…

Similar Papers 제목 키워드 기반

QIAI at MEDIQA 2021: Multimodal Radiology Report Summarization

2021-06-01 · NAACL (BioNLP) 2021 6 · Jean-Benoit Delbrouck, Cassie Zhang, Daniel Rubin

This paper describes the solution of the QIAI lab sent to the Radiology Report Summarization (RRS) challenge at MEDIQA 2021. This paper aims to investigate whether using multimodality during training improves the summari…

Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning

2021-06-01 · NAACL (BioNLP) 2021 6 · Ravi Kondadadi, Sahil Manchanda, Jason Ngo, Ronan McCormack

This paper describes experiments undertaken and their results as part of the BioNLP MEDIQA 2021 challenge. We participated in Task 3: Radiology Report Summarization. Multiple runs were submitted for evaluation, from solu…

Abstractive Text SummarizationTransfer Learning

IBMResearch at MEDIQA 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization

2021-06-01 · NAACL (BioNLP) 2021 6 · Diwakar Mahajan, Ching-Huei Tsou, Jennifer J Liang

Although recent advances in abstractive summarization systems have achieved high scores on standard natural language metrics like ROUGE, their lack of factual consistency remains an open challenge for their use in sensit…

Abstractive Text SummarizationDecoderRe-Ranking

Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain

2021-06-01 · NAACL (BioNLP) 2021 6 · Asma Ben Abacha, Yassine Mrabet, Yuhao Zhang, Chaitanya Shivade 외

The MEDIQA 2021 shared tasks at the BioNLP 2021 workshop addressed three tasks on summarization for medical text: (i) a question summarization task aimed at exploring new approaches to understanding complex real-world co…

Text Summarization

paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization

2021-06-01 · NAACL (BioNLP) 2021 6 · Wei Zhu, Yilong He, Ling Chai, Yunxiao Fan 외

In this article, we describe our systems for the MEDIQA 2021 Shared Tasks. First, we will describe our method for the second task, Multi-Answer Summarization (MAS). For extractive summarization, two series of methods are…

Extractive SummarizationSentence