# 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, # # }