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