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import re |
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import os |
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import logging |
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import pickle |
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import argparse |
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import numpy as np |
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import pandas as pd |
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from tqdm import tqdm |
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def make_summarization_csv(args): |
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if args.for_qfid: |
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logging.info('Making csv files for QFiD...') |
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logging.info('Columns={"reference": literature review title <s> chapter title </s> literature review title <s> chapter title <s> abstract of cited paper 1 <s> BIB001 </s> literature review title <s> chapter title <s> abstract of cited paper 2 <s> BIB002 </s> ..., "target": literature review chapter}') |
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else: |
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logging.info('Making csv files for summarization...') |
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logging.info('Columns={"reference": literature review title <s> chapter title <s> abstract of cited paper 1 <s> BIB001 </s> literature review title <s> chapter title <s> abstract of cited paper 2 <s> BIB002 </s> ..., "target": literature review chapter}') |
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section_df = pd.read_pickle(os.path.join(args.dataset_path, 'split_survey_df.pkl')) |
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dataset_df = section_df[section_df['n_bibs'].apply(lambda n_bibs: n_bibs >= 2)] |
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dataset_df = dataset_df.rename(columns={'text': 'target'}) |
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dataset_df = dataset_df.rename(columns={'bib_cinting_sentences': 'bib_citing_sentences'}) |
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dataset_df['reference'] = dataset_df[['bib_abstracts', 'section', 'title']].apply(lambda bib_abstracts: ' '.join(['</s> {} <s> {} <s> {} <s> BIB{}'.format(bib_abstracts[2], bib_abstracts[1], abstract, bib) for bib, abstract in bib_abstracts[0].items()]), axis=1) |
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if args.for_qfid: |
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dataset_df['reference'] = dataset_df['title'] + ' <s> ' + dataset_df['section'] + ' ' + dataset_df['reference'] |
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else: |
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dataset_df['reference'] = dataset_df['reference'].apply(lambda s: s[5:]) |
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split_df = dataset_df['split'] |
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dataset_df = dataset_df[['reference', 'target']] |
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train_df = dataset_df[split_df == 'train'] |
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val_df = dataset_df[split_df == 'val'] |
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test_df = dataset_df[split_df == 'test'] |
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if args.for_qfid: |
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train_df.to_csv(os.path.join(args.dataset_path, 'train_qfid.csv'), index=False) |
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val_df.to_csv(os.path.join(args.dataset_path, 'val_qfid.csv'), index=False) |
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test_df.to_csv(os.path.join(args.dataset_path, 'test_qfid.csv'), index=False) |
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else: |
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train_df.to_csv(os.path.join(args.dataset_path, 'train.csv'), index=False) |
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val_df.to_csv(os.path.join(args.dataset_path, 'val.csv'), index=False) |
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test_df.to_csv(os.path.join(args.dataset_path, 'test.csv'), index=False) |
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logging.info('Done!') |
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def anonymize_bib(args): |
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logging.info('Converting BIB identifiers...') |
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for split in ['val', 'test', 'train']: |
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if args.for_qfid: |
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df = pd.read_csv(os.path.join(args.dataset_path, '{}_qfid.csv'.format(split))) |
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else: |
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df = pd.read_csv(os.path.join(args.dataset_path, '{}.csv'.format(split))) |
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bar = tqdm(total=len(df)) |
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for row in df.itertuples(): |
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cnt = 1 |
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bib_dict = {} |
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for i in range(len(row.reference)): |
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if row.reference[i:i+7] == '<s> BIB': |
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bib_dict[row.reference[i+7:].split(' ')[0]] = cnt |
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cnt += 1 |
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ref = row.reference |
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tgt = row.target |
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for key, value in bib_dict.items(): |
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ref = re.sub('BIB{}'.format(key), 'BIB{:0>3}'.format(value), ref) |
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tgt = re.sub('BIB{}'.format(key), 'BIB{:0>3}'.format(value), tgt) |
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df.at[row.Index, 'reference'] = ref |
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df.at[row.Index, 'target'] = tgt |
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bar.update(1) |
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logging.info('Saving...') |
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if args.for_qfid: |
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df.to_csv(os.path.join(args.dataset_path, '{}_qfid.csv'.format(split)), index=False) |
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else: |
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df.to_csv(os.path.join(args.dataset_path, '{}.csv'.format(split)), index=False) |
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if __name__ == '__main__': |
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logging.basicConfig(format='%(message)s', level=logging.DEBUG) |
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parser = argparse.ArgumentParser(description='') |
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parser.add_argument('-dataset_path', default=".", help='Path to the generated dataset') |
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parser.add_argument('--for_qfid', action='store_true', help='Add if you train QFiD on the generated csv files') |
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args = parser.parse_args() |
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make_summarization_csv(args) |
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anonymize_bib(args) |
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