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   "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"
   ]
  }
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