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data/validation-00000-of-00001-6393d2add96db558.parquet → CSAbstruct/csabstruct-test.parquet

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data/train-00000-of-00001-e4ddac953345ce34.parquet → CSAbstruct/csabstruct-train.parquet

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data/test-00000-of-00001-be7e891381aedbe0.parquet → CSAbstruct/csabstruct-validation.parquet

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README.md

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----
-license: apache-2.0
----
-
-
-# CSAbstruct
-
-CSAbstruct was created as part of *"Pretrained Language Models for Sequential Sentence Classification"* ([ACL Anthology][2], [arXiv][1], [GitHub][6]).
-
-It contains 2,189 manually annotated computer science abstracts with sentences annotated according to their rhetorical roles in the abstract, similar to the [PUBMED-RCT][3] categories.
-
-
-## Dataset Construction Details
-
-CSAbstruct is a new dataset of annotated computer science abstracts with sentence labels according to their rhetorical roles.
-The key difference between this dataset and [PUBMED-RCT][3] is that PubMed abstracts are written according to a predefined structure, whereas computer science papers are free-form.
-Therefore, there is more variety in writing styles in CSAbstruct.
-CSAbstruct is collected from the Semantic Scholar corpus [(Ammar et a3., 2018)][4].
-E4ch sentence is annotated by 5 workers on the [Figure-eight platform][5], with one of 5 categories `{BACKGROUND, OBJECTIVE, METHOD, RESULT, OTHER}`.
-
-We use 8 abstracts (with 51 sentences) as test questions to train crowdworkers.
-Annotators whose accuracy is less than 75% are disqualified from doing the actual annotation job.
-The annotations are aggregated using the agreement on a single sentence weighted by the accuracy of the annotator on the initial test questions.
-A confidence score is associated with each instance based on the annotator initial accuracy and agreement of all annotators on that instance.
-We then split the dataset 75%/15%/10% into train/dev/test partitions, such that the test set has the highest confidence scores.
-Agreement rate on a random subset of 200 sentences is 75%, which is quite high given the difficulty of the task.
-Compared with [PUBMED-RCT][3], our dataset exhibits a wider variety of writ- ing styles, since its abstracts are not written with an explicit structural template.
-
-## Dataset Statistics
-
-| Statistic                |  Avg ± std  |
-|--------------------------|-------------|
-| Doc length in sentences  |  6.7 ± 1.99 |
-| Sentence length in words | 21.8 ± 10.0 |
-
-| Label         | % in Dataset |
-|---------------|--------------|
-| `BACKGROUND`  | 33%          |
-| `METHOD`      | 32%          |
-| `RESULT`      | 21%          |
-| `OBJECTIVE`   | 12%          |
-| `OTHER`       | 03%          |
-
-## Citation
-
-If you use this dataset, please cite the following paper:
-
-```
-@inproceedings{Cohan2019EMNLP,
-  title={Pretrained Language Models for Sequential Sentence Classification},
-  author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld},
-  year={2019},
-  booktitle={EMNLP},
-}
-```
-
-[1]: https://arxiv.org/abs/1909.04054
-[2]: https://aclanthology.org/D19-1383
-[3]: https://github.com/Franck-Dernoncourt/pubmed-rct
-[4]: https://aclanthology.org/N18-3011/
-[5]: https://www.figure-eight.com/
-[6]: https://github.com/allenai/sequential_sentence_classification

csabstruct.py

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-"""
-Dataset from https://github.com/allenai/sequential_sentence_classification
-
-Dataset maintainer: @soldni
-"""
-
-
-import json
-from typing import Iterable, Sequence, Tuple
-
-import datasets
-from datasets.builder import BuilderConfig, GeneratorBasedBuilder
-from datasets.info import DatasetInfo
-from datasets.splits import Split, SplitGenerator
-from datasets.utils.logging import get_logger
-
-LOGGER = get_logger(__name__)
-
-
-_NAME = "CSAbstruct"
-_CITATION = """\
-@inproceedings{Cohan2019EMNLP,
-  title={Pretrained Language Models for Sequential Sentence Classification},
-  author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld},
-  year={2019},
-  booktitle={EMNLP},
-}
-"""
-_LICENSE = "Apache License 2.0"
-_DESCRIPTION = """\
-As a step toward better document-level understanding, we explore \
-classification of a sequence of sentences into their corresponding \
-categories, a task that requires understanding sentences in context \
-of the document. Recent successful models for this task have used \
-hierarchical models to contextualize sentence representations, and \
-Conditional Random Fields (CRFs) to incorporate dependencies between \
-subsequent labels. In this work, we show that pretrained language \
-models, BERT (Devlin et al., 2018) in particular, can be used for \
-this task to capture contextual dependencies without the need for \
-hierarchical encoding nor a CRF. Specifically, we construct a joint \
-sentence representation that allows BERT Transformer layers to \
-directly utilize contextual information from all words in all \
-sentences. Our approach achieves state-of-the-art results on four \
-datasets, including a new dataset of structured scientific abstracts.
-"""
-_HOMEPAGE = "https://github.com/allenai/sequential_sentence_classification"
-_VERSION = "1.0.0"
-
-_URL = (
-    "https://raw.githubusercontent.com/allenai/"
-    "sequential_sentence_classification/master/"
-)
-
-_SPLITS = {
-    Split.TRAIN: _URL + "data/CSAbstruct/train.jsonl",
-    Split.VALIDATION: _URL + "data/CSAbstruct/dev.jsonl",
-    Split.TEST: _URL + "data/CSAbstruct/test.jsonl",
-}
-
-
-class CSAbstruct(GeneratorBasedBuilder):
-    """CSAbstruct"""
-
-    BUILDER_CONFIGS = [
-        BuilderConfig(
-            name=_NAME,
-            version=datasets.Version(_VERSION),
-            description=_DESCRIPTION,
-        )
-    ]
-
-    def _info(self) -> DatasetInfo:
-        class_labels = ["background", "method", "objective", "other", "result"]
-
-        features = datasets.Features(
-            {
-                "abstract_id": datasets.Value("string"),
-                "sentences": [datasets.Value("string")],
-                "labels": [datasets.ClassLabel(names=class_labels)],
-                "confs": [datasets.Value("float")],
-            }
-        )
-
-        return DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(
-        self, dl_manager: datasets.DownloadManager
-    ) -> Sequence[SplitGenerator]:
-        archive = dl_manager.download(_SPLITS)
-
-        return [
-            SplitGenerator(
-                name=split_name,  # type: ignore
-                gen_kwargs={
-                    "split_name": split_name,
-                    "filepath": archive[split_name],  # type: ignore
-                },
-            )
-            for split_name in _SPLITS
-        ]
-
-    def _generate_examples(
-        self, split_name: str, filepath: str
-    ) -> Iterable[Tuple[str, dict]]:
-        """This function returns the examples in the raw (text) form."""
-
-        LOGGER.info(f"generating examples from documents in {filepath}...")
-
-        with open(filepath, mode="r", encoding="utf-8") as f:
-            data = [json.loads(ln) for ln in f]
-
-        for i, row in enumerate(data):
-            row["abstract_id"] = f"{split_name}_{i:04d}"
-            yield row["abstract_id"], row

dataset_infos.json

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-{"allenai--csabstruct": {"description": "As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to contextualize sentence representations, and Conditional Random Fields (CRFs) to incorporate dependencies between subsequent labels. In this work, we show that pretrained language models, BERT (Devlin et al., 2018) in particular, can be used for this task to capture contextual dependencies without the need for hierarchical encoding nor a CRF. Specifically, we construct a joint sentence representation that allows BERT Transformer layers to directly utilize contextual information from all words in all sentences. Our approach achieves state-of-the-art results on four datasets, including a new dataset of structured scientific abstracts.\n", "citation": "@inproceedings{Cohan2019EMNLP,\n  title={Pretrained Language Models for Sequential Sentence Classification},\n  author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld},\n  year={2019},\n  booktitle={EMNLP},\n}\n", "homepage": "https://github.com/allenai/sequential_sentence_classification", "license": "Apache License 2.0", "features": {"abstract_id": {"dtype": "string", "id": null, "_type": "Value"}, "sentences": [{"dtype": "string", "id": null, "_type": "Value"}], "labels": [{"num_classes": 5, "names": ["background", "method", "objective", "other", "result"], "id": null, "_type": "ClassLabel"}], "confs": [{"dtype": "float32", "id": null, "_type": "Value"}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "csabstruct", "config_name": "CSAbstruct", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1892682, "num_examples": 1668, "dataset_name": "csabstruct"}, "validation": {"name": "validation", "num_bytes": 335336, "num_examples": 295, "dataset_name": "csabstruct"}, "test": {"name": "test", "num_bytes": 226902, "num_examples": 226, "dataset_name": "csabstruct"}}, "download_checksums": null, "download_size": 1331967, "post_processing_size": null, "dataset_size": 2454920, "size_in_bytes": 3786887}}