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import json |
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import pandas as pd |
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import datasets |
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import os |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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@inproceedings{chen-etal-2021-dialogsum, |
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title={{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset}, |
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author={Chen, Yulong and Liu, Yang and Chen, Liang and Zhang, Yue}, |
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journal={arXiv preprint arXiv:1911.12237}, |
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year={2021}, |
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booktitle ={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021"}, |
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month = {aug}, |
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address = {Online}, |
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publisher = {Association for Computational Linguistics}, |
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url = {https://aclanthology.org/2021.findings-acl.449}, |
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doi = {10.18653/v1/2021.findings-acl.449}, |
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pages = {5062--5074} |
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} |
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""" |
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_DESCRIPTION = """ |
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DialogSUM Corpus contains 13460 chat dialogues with manually annotated |
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summaries. |
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There are two features: |
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- dialogue: text of dialogue. |
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- summary: human written summary of the dialogue. |
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- topic: one liner summary of the dialogue. |
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- id: id of a example. |
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""" |
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_HOMEPAGE = "hhttps://aclanthology.org/2021.findings-acl.449" |
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_LICENSE = "CC BY-NC-ND 4.0" |
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_URL = "https://huggingface.co/datasets/knkarthick/dialogsum_reformat/tree/main/" |
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_URL = "https://huggingface.co/datasets/knkarthick/dialogsum_reformat/resolve/main/" |
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_URLS = { |
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"train": _URL + "train.json", |
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"test": _URL + "test.json", |
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"val": _URL + "val.json", |
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} |
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class Dialogsum(datasets.GeneratorBasedBuilder): |
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"""DialogSum Corpus dataset.""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="dialogsum_reformat", |
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version=datasets.Version("1.0.0", ""), |
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description="DialogSum Corpus dataset", |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"dialogue": datasets.Value("string"), |
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"summary": datasets.Value("string"), |
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"topic": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["test"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}), |
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] |
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def _generate_examples(self, filepath, split): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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key = 0 |
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with open(os.path.join(filepath, split)) as f : |
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data = json.load(f) |
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for info in data : |
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dialogue_id = info['id'] |
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dialogue_name = info['dialogue'] |
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dialogue_summary = info['summary'] |
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dialogue_topic = info['topic'] |
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yield key, { |
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"id" : dialogue_id, |
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"dialogue" : dialogue_name, |
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"summary" : dialogue_summary, |
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"topic" : dialogue_topic, |
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} |
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key += 1 |
