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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "cf6af8de-b9f8-4396-8496-9f1d28dd6156",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done! Created EASC.csv and EASC.jsonl\n"
]
}
],
"source": [
"import os\n",
"import re\n",
"import json\n",
"import pandas as pd\n",
"\n",
"ARTICLES_DIR = \"Articles\"\n",
"MTURK_DIR = \"MTurk\"\n",
"\n",
"records_csv = []\n",
"records_jsonl = []\n",
"\n",
"for folder in sorted(os.listdir(ARTICLES_DIR)):\n",
" folder_path = os.path.join(ARTICLES_DIR, folder)\n",
" if not os.path.isdir(folder_path):\n",
" continue\n",
"\n",
" # Extract article_id from folder name \"Article001\"\n",
" m = re.match(r\"Article(\\d+)\", folder)\n",
" if not m:\n",
" print(f\"Skipping folder (no article id): {folder}\")\n",
" continue\n",
"\n",
" article_id = int(m.group(1))\n",
"\n",
" # Read article text + topic name (from file name)\n",
" article_files = os.listdir(folder_path)\n",
" if len(article_files) == 0:\n",
" print(f\"No article file in {folder}\")\n",
" continue\n",
"\n",
" article_file = article_files[0] # only one expected\n",
" article_file_path = os.path.join(folder_path, article_file)\n",
"\n",
" # Extract topic name from filename \"Science and Tech (8).txt\"\n",
" base = os.path.splitext(article_file)[0]\n",
" match = re.match(r\"(.+?)\\s*\\(\\d+\\)\", base)\n",
" topic_name = match.group(1).strip() if match else \"Unknown\"\n",
"\n",
" # Read article text (strict UTF-8)\n",
" with open(article_file_path, \"r\", encoding=\"utf-8\", errors=\"replace\") as f:\n",
" article_text = f.read().strip()\n",
"\n",
" # Read MTurk summaries for the same article\n",
" summaries_dir = os.path.join(MTURK_DIR, folder)\n",
" if not os.path.isdir(summaries_dir):\n",
" print(f\"No MTurk summaries for {folder}\")\n",
" continue\n",
"\n",
" summary_files = sorted(os.listdir(summaries_dir))\n",
" summaries = []\n",
"\n",
" for sfile in summary_files:\n",
" s_path = os.path.join(summaries_dir, sfile)\n",
" with open(s_path, \"r\", encoding=\"utf-8\", errors=\"replace\") as f:\n",
" summaries.append(f.read().strip())\n",
"\n",
" # Ensure exactly 5 summaries (MTurk provides 5)\n",
" while len(summaries) < 5:\n",
" summaries.append(\"\")\n",
"\n",
" # Trim excess if more than 5 (rare)\n",
" summaries = summaries[:5]\n",
"\n",
" # --- CSV ROW ---\n",
" records_csv.append({\n",
" \"article_id\": article_id,\n",
" \"topic_name\": topic_name,\n",
" \"article_text\": article_text,\n",
" \"summary_A\": summaries[0],\n",
" \"summary_B\": summaries[1],\n",
" \"summary_C\": summaries[2],\n",
" \"summary_D\": summaries[3],\n",
" \"summary_E\": summaries[4]\n",
" })\n",
"\n",
" # --- JSONL ROW ---\n",
" records_jsonl.append({\n",
" \"article_id\": article_id,\n",
" \"topic_name\": topic_name,\n",
" \"article_text\": article_text,\n",
" \"summaries\": summaries\n",
" })\n",
"\n",
"# Save CSV (UTF-8)\n",
"df = pd.DataFrame(records_csv)\n",
"df.to_csv(\"EASC.csv\", index=False, encoding=\"utf-8\")\n",
"\n",
"# Save JSONL (UTF-8)\n",
"with open(\"EASC.jsonl\", \"w\", encoding=\"utf-8\") as f:\n",
" for row in records_jsonl:\n",
" f.write(json.dumps(row, ensure_ascii=False) + \"\\n\")\n",
"\n",
"print(\"Done! Created EASC.csv and EASC.jsonl\")\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "599e5c36-b09f-4dc3-b79e-3e418c67f318",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"df = pd.read_csv(\"EASC.csv\")\n",
"\n",
"train_df, temp_df = train_test_split(df, test_size=0.2, random_state=42)\n",
"val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
"\n",
"train_df.to_csv(\"EASC_train.csv\", index=False)\n",
"val_df.to_csv(\"EASC_val.csv\", index=False)\n",
"test_df.to_csv(\"EASC_test.csv\", index=False)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat": 4,
"nbformat_minor": 5
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