license:afl-3.0 region:us
MEDIQA @ NAACL-BioNLP 2021 -- Task 2: Multi-answer summarization

https://sites.google.com/view/mediqa2021

Training Data

The MEDIQA-AnS Dataset could be used for training.

Participants can use available external resources such as existing medical QA datasets.

Validation and Test Sets

The original answers are generated by the consumer health question answering system CHiQA which searches for answers from only trustworthy medical information sources. The summaries are manually created by medical experts.

The validation set contains 192 answers associated with 50 questions. Each question has at least two answers and their summaries. For each question, we provide two types of summaries: extractive and abstractive. We encourage the use of all types of summarization approaches (extractive, abstractive, and hybrid). We will also provide the questions in the official test set as they can be used as additional inputs for the summarization models.

The test set contains 303 answers associated with 80 questions. For each test question, we provide two reference summaries: extractive and abstractive. ** In the official competiton, we used the abstractive reference summaries to evaluate the abstractive systems and the extractive summaries to evaluate the extractive systems (only extractive summaries were used on AIcrowd-Task2).
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license: afl-3.0
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## MEDIQUA2012-MAS task

source data is available [here](https://github.com/abachaa/MEDIQA2021/tree/main/Task2)

des:

1. data features

Multiple Answer Summarization with:

* key: key of each question
* question: question
* text: merge all text of all answers (if it is train-split, a merge of article and section part)
* sum\_abs: abstractive multiple answer summarization 
* sum\_ext: extractive multiple answer summarization 

2. train\_article /  train\_sec

Same structure with train, but:

* train: text: merge all text of all answers (if it is train-split, a merge of article and section part)
* train\_article: text is a merge of all subanswers 's articles
* train\_sec: text is a merge of all subanswers 's sections
