MNLP at MEDIQA 2021: Fine-Tuning PEGASUS for Consumer Health Question Summarization
This paper details a Consumer Health Question (CHQ) summarization model submitted to MEDIQA 2021 for shared task 1: Question Summarization. Many CHQs are composed of multiple sentences with typos or unnecessary information, which can interfere with automated question answering systems. Question summarization mitigates this issue by removing this unnecessary information, aiding automated systems in generating a more accurate summary. Our summarization approach focuses on applying multiple pre-processing techniques, including question focus identification on the input and the development of an ensemble method to combine question focus with an abstractive summarization method. We use the state-of-art abstractive summarization model, PEGASUS (Pre-training with Extracted Gap-sentences for Abstractive Summarization), to generate abstractive summaries. Our experiments show that using our ensemble method, which combines abstractive summarization with question focus identification, improves performance over using summarization alone. Our model shows a ROUGE-2 F-measure of 11.14% against the official test dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Abstractive Text SummarizationQuestion AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
NCUEE-NLP at MEDIQA 2021: Health Question Summarization Using PEGASUS Transformers
This study describes the model design of the NCUEE-NLP system for the MEDIQA challenge at the BioNLP 2021 workshop. We use the PEGASUS transformers and fine-tune the downstream summarization task using our collected and …
WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers
This paper describes our contribution for the MEDIQA-2021 Task 1 question summarization competition. We model the task as conditional generation problem. Our concrete pipeline performs a finetuning of the large pretraine…
Entity EmbeddingsSB_NITK at MEDIQA 2021: Leveraging Transfer Learning for Question Summarization in Medical Domain
Recent strides in the healthcare domain, have resulted in vast quantities of streaming data available for use for building intelligent knowledge-based applications. However, the challenges introduced to the huge volume, …
Transfer LearningAUTH @ CLSciSumm 20, LaySumm 20, LongSumm 20
We present the systems we submitted for the shared tasks of the Workshop on Scholarly Document Processing at EMNLP 2020. Our approaches to the tasks are focused on exploiting large Transformer models pre-trained on huge …
NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization
The quest for seeking health information has swamped the web with consumers’ healthrelated questions, which makes the need for efficient and reliable question answering systems more pressing. The consumers’ questions, ho…
DescriptiveQuestion AnsweringRetrievalTransfer Learning