paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization
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 applied. The first one follows (CITATION). First a RoBERTa model is first applied to give a local ranking of the candidate sentences. Then a Markov Chain model is applied to evaluate the sentences globally. The second method applies cross-sentence contextualization to improve the local ranking and discard the global ranking step. Our methods achieve the 1st Place in the MAS task. For the question summarization (QS) and radiology report summarization (RRS) tasks, we explore how end-to-end pre-trained seq2seq model perform. A series of tricks for improving the fine-tuning performances are validated.
Code (0)
등록된 구현이 없습니다.
Tasks
Extractive SummarizationSentenceSimilar Papers 제목 키워드 기반
UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization
This paper describes a system developed to summarize multiple answers challenge in the MEDIQA 2021 shared task collocated with the BioNLP 2021 Workshop. We present an abstractive summarization model based on BART, a deno…
Abstractive Text SummarizationDenoisingChicHealth @ MEDIQA 2021: Exploring the limits of pre-trained seq2seq models for medical summarization
In this article, we will describe our system for MEDIQA2021 shared tasks. First, we will describe the method of the second task, multiple answer summary (MAS). For extracting abstracts, we follow the rules of (CITATION).…
TranslationFG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG
Retrieval-Augmented Generation (RAG) enables large language models to provide more precise and pertinent responses by incorporating external knowledge. In the Query-Focused Summarization (QFS) task, GraphRAG-based approa…
DiversityQuery-focused SummarizationRAGResponse Generation+2MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation
This work introduces MediQAl, a French medical question answering dataset designed to evaluate the capabilities of language models in factual medical recall and reasoning over real-world clinical scenarios. MediQAl conta…
Question AnsweringNCUEE-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 …