dialogsum_reformat.py

# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""DIALOGSum dataset."""


import json

import py7zr

import datasets


_CITATION = """
@inproceedings{chen-etal-2021-dialogsum,
  title={{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset},
  author={Chen, Yulong and Liu, Yang  and Chen, Liang  and Zhang, Yue},
  journal={arXiv preprint arXiv:1911.12237},
  year={2021},
  booktitle ={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021"},
  month = {aug},
  address = {Online},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2021.findings-acl.449},
  doi = {10.18653/v1/2021.findings-acl.449},
  pages = {5062--5074}
}
"""

_DESCRIPTION = """
DialogSUM Corpus contains 13460 chat dialogues with manually annotated
summaries.
There are two features:
  - dialogue: text of dialogue.
  - summary: human written summary of the dialogue.
  - topic: one liner summary of the dialogue.
  - id: id of a example.
"""
_HOMEPAGE = "hhttps://aclanthology.org/2021.findings-acl.449"
_LICENSE = "CC BY-NC-ND 4.0"
_URL = "https://huggingface.co/datasets/knkarthick/dialogsum_reformat/resolve/main/corpus.7z"


class Dialogsum(datasets.GeneratorBasedBuilder):
    """DIALOGSum Corpus dataset."""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="dialogsum"),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "dialogue": datasets.Value("string"),
                "summary": datasets.Value("string"),
                "topic": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        path = dl_manager.download(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": (path, "train.json"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": (path, "test.json"),
                    "split": "test",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": (path, "val.json"),
                    "split": "val",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        """Yields examples."""
        path, fname = filepath
        with open(path, "rb") as f:
            with py7zr.SevenZipFile(f, "r") as z:
                for name, bio in z.readall().items():
                    if name == fname:
                        data = json.load(bio)
        for example in data:
            yield example["id"], example

###################### OLD #####################

# import json
# import pandas as pd
# import datasets
# import os

# logger = datasets.logging.get_logger(__name__)

# _CITATION = """
# @inproceedings{chen-etal-2021-dialogsum,
#   title={{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset},
#   author={Chen, Yulong and Liu, Yang  and Chen, Liang  and Zhang, Yue},
#   journal={arXiv preprint arXiv:1911.12237},
#   year={2021},
#   booktitle ={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021"},
#   month = {aug},
#   address = {Online},
#   publisher = {Association for Computational Linguistics},
#   url = {https://aclanthology.org/2021.findings-acl.449},
#   doi = {10.18653/v1/2021.findings-acl.449},
#   pages = {5062--5074}
# }
# """

# _DESCRIPTION = """
# DialogSUM Corpus contains 13460 chat dialogues with manually annotated
# summaries.
# There are two features:
#   - dialogue: text of dialogue.
#   - summary: human written summary of the dialogue.
#   - topic: one liner summary of the dialogue.
#   - id: id of a example.
# """
# _HOMEPAGE = "hhttps://aclanthology.org/2021.findings-acl.449"
# _LICENSE = "CC BY-NC-ND 4.0"
# # _URL = "https://huggingface.co/datasets/knkarthick/dialogsum_reformat/tree/main/"
# _URL = "https://huggingface.co/datasets/knkarthick/dialogsum_reformat/resolve/main/"
# # _URL = "https://huggingface.co/datasets/knkarthick/dialogsum_reformat/blob/main/"

# _URLS = {
#     "train": _URL + "train.json",
#     "test": _URL + "test.json",
#     "val": _URL + "val.json",
# }



# class Dialogsum(datasets.GeneratorBasedBuilder):
#     """Dialogsum Corpus dataset."""

#     VERSION = datasets.Version("1.1.0")

#     BUILDER_CONFIGS = [
#         datasets.BuilderConfig(name="dialogsum_reformat"),
#     ]

#     def _info(self):
#         return datasets.DatasetInfo(
#             description=_DESCRIPTION,
#             features=datasets.Features(
#                 {
#                     "id": datasets.Value("string"),
#                     "dialogue": datasets.Value("string"),
#                     "summary": datasets.Value("string"),
#                     "topic": datasets.Value("string"),
#                 }
#             ),
#             # No default supervised_keys (as we have to pass both question
#             # and context as input).
#             supervised_keys=None,
#             homepage=_HOMEPAGE,
#             license=_LICENSE,
#             citation=_CITATION,
#         )

#     def _split_generators(self, dl_manager):
#         downloaded_files = dl_manager.download_and_extract(_URLS)
#         return [
#             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
#             datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
#             datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
#         ]

#     def _generate_examples(self, filepath):
#         """This function returns the examples in the raw (text) form."""
#         logger.info("generating examples from = %s", filepath)
#         with open(filepath) as f :
#     		data = json.load(f)

#         for info in data :
#             dialogue_id = info['id']
#             dialogue_name = info['dialogue']
#             dialogue_summary = info['summary']
#             dialogue_topic = info['topic']

#             yield {
#                 "id" : dialogue_id,
#                 "dialogue" : dialogue_name,
#                 "summary" : dialogue_summary,
#                 "topic" : dialogue_topic,
#             }
            
#     # def _generate_examples(self, filepath, split):
#     #     """This function returns the examples in the raw (text) form."""
#     #     logger.info("generating examples from = %s", filepath)
#     #     with open(os.path.join(filepath, split)) as f :
#     # 		data = json.load(f)

#     #     for info in data :
#     #         dialogue_id = info['id']
#     #         dialogue_name = info['dialogue']
#     #         dialogue_summary = info['summary']
#     #         dialogue_topic = info['topic']

#     #         yield key, {
#     #             "id" : dialogue_id,
#     #             "dialogue" : dialogue_name,
#     #             "summary" : dialogue_summary,
#     #             "topic" : dialogue_topic,
#     #         }