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MeQSum

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Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    FileNotFoundError
Message:      Couldn't find a dataset script at /src/services/worker/sumedh/MeQSum/MeQSum.py or any data file in the same directory. Couldn't find 'sumedh/MeQSum' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in sumedh/MeQSum. 
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response
                  for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory
                  raise FileNotFoundError(
              FileNotFoundError: Couldn't find a dataset script at /src/services/worker/sumedh/MeQSum/MeQSum.py or any data file in the same directory. Couldn't find 'sumedh/MeQSum' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in sumedh/MeQSum.

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  • Problem type: Summarization

languages:

  • en

multilinguality:

  • monolingual

task_ids:

  • summarization

MeQSum

Dataset for medical question summarization introduced in the ACL 2019 paper "On the Summarization of Consumer Health Questions": https://www.aclweb.org/anthology/P19-1215

Citation Information 

@Inproceedings{MeQSum,
author = {Asma {Ben Abacha} and Dina Demner-Fushman},
title = {On the Summarization of Consumer Health Questions},
booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28th - August 2},
year = {2019},
abstract = {Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16%. We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization. }}
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