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--- |
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license: mit |
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task_categories: |
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- summarization |
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- text2text-generation |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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source_datasets: tomasg25/scientific_lay_summarisation |
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--- |
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# scientific_lay_summarisation - PLOS - normalized |
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This dataset is a modified version of [tomasg25/scientific_lay_summarization](https://huggingface.co/datasets/tomasg25/scientific_lay_summarisation) and contains scientific lay summaries that have been preprocessed [with this code](https://gist.github.com/pszemraj/bd344637af7c0c10ecf4ab62c4d0ce91). The preprocessing includes fixing punctuation and whitespace problems, and calculating the token length of each text sample using a tokenizer from the T5 model. |
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Original dataset details: |
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- **Repository:** https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation |
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- **Paper:** [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932) |
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## Data Cleaning |
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The text in both the "article" and "summary" columns was processed to ensure that punctuation and whitespace were consistent. The `fix_punct_whitespace` function was applied to each text sample to: |
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- Remove spaces before punctuation marks (except for parentheses) |
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- Add a space after punctuation marks (except for parentheses) if missing |
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- Handle spaces around parentheses |
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- Add a space after a closing parenthesis if followed by a word or opening parenthesis |
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- Handle spaces around quotation marks |
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- Handle spaces around single quotes |
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- Handle comma in numbers |
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## Tokenization |
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The length of each text sample was calculated in terms of tokens using the T5 tokenizer. The `calculate_token_length` function was used to encode each text sample using the tokenizer and return the number of resulting tokens. The resulting token lengths were added as new columns to the dataframes. |
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## Data Format |
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The resulting processed data files are stored in Apache parquet and can be loaded using the `pandas' library or the `datasets' library from the Hugging Face transformers package. The relevant column names and data types for summarization are |
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```python |
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DatasetDict({ |
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train: Dataset({ |
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features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'], |
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num_rows: 24773 |
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}) |
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test: Dataset({ |
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features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'], |
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num_rows: 1376 |
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}) |
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validation: Dataset({ |
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features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'], |
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num_rows: 1376 |
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}) |
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}) |
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``` |
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## Usage |
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Load the desired parquet file(s) using `pandas` or `datasets`. Here is an example using `pandas`: |
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```python |
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# download the dataset files by clicking on 'use in datasets' and cloning |
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import pandas as pd |
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# Load train set |
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df = pd.read_parquet("scientific_lay_summarisation-plos-norm/train.parquet") |
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print(df.info()) |
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``` |
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And here is an example using `datasets`: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("pszemraj/scientific_lay_summarisation-plos-norm") |
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train_set = dataset['train'] |
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# Print the first few samples |
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for i in range(5): |
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print(train_set[i]) |
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``` |
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## Token Lengths |
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For train split: |
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--- |
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