PaperSummarizer.ipynb

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              "2024-06-22 - Bone Fracture Classification using Transfer Learning (Publisher: arXiv)",
              "2024-06-01 - 3DFRINet: A Framework for the Detection and Diagnosis of Fracture Related Infection in Low Extremities Based on <sup>18</sup>F-FDG PET/CT 3D Images. (Publisher: PubMed)",
              "2024-05-31 - Deep learning-based automated high-accuracy location and identification of fresh vertebral compression fractures from spinal radiographs: a multicenter cohort study. (Publisher: PubMed)",
              "2024-05-26 - Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics. (Publisher: PubMed)",
              "2024-05-09 - Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle. (Publisher: PubMed)",
              "2024-04-15 - Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images. (Publisher: PubMed)",
              "2024-03-29 - A multi-stage semi-supervised learning for ankle fracture classification on CT images (Publisher: arXiv)",
              "2023-05-26 - Shape-based pose estimation for automatic standard views of the knee (Publisher: arXiv)",
              "2023-02-02 - Human not in the loop: objective sample difficulty measures for Curriculum Learning (Publisher: arXiv)",
              "2022-10-20 - Semi-supervised object detection based on single-stage detector for thighbone fracture localization (Publisher: arXiv)"
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            "value": "<b>Title:</b> Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle.<br><b>Published:</b> 2024-05-09<br><b>Publisher:</b> PubMed<br><b>URL:</b> <a href='https://pubmed.ncbi.nlm.nih.gov/38724896/' target='_blank'>Click here to view the article</a><br><br><b>Summary:</b><br>Research intends to enhance the diagnosis of diseases of the auriculotemporal region and ossicles by combining High-Resolution Spiral Computed Tomography (HRSCT) scanning with Deep Learning Techniques (DLT) This study employs a deep learning method, Convolutional Neural Network-UNet, to extract sub-pixel information from medical photos. This method equips doctors and researchers with cutting-edge resources."
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      "source": [
        "# Install necessary libraries\n",
        "!pip install arxiv pandas biopython transformers ipywidgets\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import arxiv\n",
        "import pandas as pd\n",
        "from Bio import Entrez\n",
        "from transformers import pipeline\n",
        "import ipywidgets as widgets\n",
        "from IPython.display import display\n",
        "from datetime import datetime\n",
        "\n",
        "# Set up Entrez email (required for NCBI's E-utilities)\n",
        "Entrez.email = \"your.email@example.com\"  # Replace with your email\n",
        "\n",
        "# Function to fetch papers from PubMed\n",
        "def fetch_papers_pubmed(query='deep learning AND classification AND fracture AND bone', max_results=5):\n",
        "    search_handle = Entrez.esearch(db=\"pubmed\", term=query, retmax=max_results)\n",
        "    search_results = Entrez.read(search_handle)\n",
        "    search_handle.close()\n",
        "\n",
        "    id_list = search_results[\"IdList\"]\n",
        "\n",
        "    papers = []\n",
        "    if id_list:\n",
        "        fetch_handle = Entrez.efetch(db=\"pubmed\", id=id_list, rettype=\"medline\", retmode=\"xml\")\n",
        "        abstracts = Entrez.read(fetch_handle)\n",
        "        fetch_handle.close()\n",
        "\n",
        "        for article in abstracts['PubmedArticle']:\n",
        "            article_title = article['MedlineCitation']['Article']['ArticleTitle']\n",
        "            abstract = ' '.join(article['MedlineCitation']['Article'].get('Abstract', {}).get('AbstractText', ['']))\n",
        "\n",
        "            publish_date = article['MedlineCitation'].get('DateCompleted') or article['MedlineCitation'].get('DateRevised') or None\n",
        "            if publish_date:\n",
        "                publish_date = f\"{publish_date['Year']}-{publish_date['Month']}-{publish_date['Day']}\"\n",
        "            else:\n",
        "                publish_date = \"Unknown\"\n",
        "\n",
        "            article_id = article['MedlineCitation']['PMID']  # Get PubMed ID\n",
        "            article_url = f\"https://pubmed.ncbi.nlm.nih.gov/{article_id}/\"  # Construct the PubMed URL\n",
        "\n",
        "            papers.append({\n",
        "                'published': publish_date,\n",
        "                'title': article_title,\n",
        "                'abstract': abstract,\n",
        "                'url': article_url,\n",
        "                'publisher': 'PubMed'\n",
        "            })\n",
        "\n",
        "    return pd.DataFrame(papers)\n",
        "\n",
        "# Function to fetch papers from arXiv\n",
        "def fetch_papers_arxiv(query='deep learning AND classification AND fracture AND bone', max_results=5):\n",
        "    search = arxiv.Search(\n",
        "        query=query,\n",
        "        max_results=max_results,\n",
        "        sort_by=arxiv.SortCriterion.SubmittedDate\n",
        "    )\n",
        "\n",
        "    papers = []\n",
        "    for result in search.results():\n",
        "        article_url = result.entry_id  # Get arXiv URL\n",
        "        papers.append({\n",
        "            'published': result.published.strftime('%Y-%m-%d'),\n",
        "            'title': result.title,\n",
        "            'abstract': result.summary,\n",
        "            'url': article_url,\n",
        "            'categories': ','.join(result.categories),\n",
        "            'publisher': 'arXiv'\n",
        "        })\n",
        "\n",
        "    return pd.DataFrame(papers)\n",
        "\n",
        "# Fetch papers from both arXiv and PubMed\n",
        "df_arxiv = fetch_papers_arxiv(max_results=5)\n",
        "df_pubmed = fetch_papers_pubmed(max_results=5)\n",
        "\n",
        "# Combine both dataframes\n",
        "df_combined = pd.concat([df_arxiv, df_pubmed], ignore_index=True)\n",
        "\n",
        "# Convert the published date to datetime for proper sorting\n",
        "df_combined['published'] = pd.to_datetime(df_combined['published'], errors='coerce')\n",
        "\n",
        "# Sort the combined dataframe by the published date\n",
        "df_combined = df_combined.sort_values(by='published', ascending=False).reset_index(drop=True)\n",
        "\n",
        "# Convert 'published' column to string format for dropdown display\n",
        "df_combined['published_str'] = df_combined['published'].dt.strftime('%Y-%m-%d')\n",
        "\n",
        "# Initialize the summarizer\n",
        "summarizer = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\", device=-1)  # Set device to 0 for GPU, -1 for CPU\n",
        "\n",
        "# Function to summarize the abstract\n",
        "def summarize_abstract(abstract_text):\n",
        "    if abstract_text:  # Check if abstract is not empty\n",
        "        summary = summarizer(abstract_text, max_length=150, min_length=30, do_sample=False)\n",
        "        return summary[0]['summary_text']\n",
        "    else:\n",
        "        return \"No abstract available.\"\n",
        "\n",
        "# Apply summarization\n",
        "df_combined['summary'] = df_combined['abstract'].apply(summarize_abstract)\n",
        "\n",
        "# Prepare dropdown options with publication date and publisher\n",
        "dropdown_options = [\n",
        "    f\"{row['published_str']} - {row['title']} (Publisher: {row['publisher']})\"\n",
        "    for _, row in df_combined.iterrows()\n",
        "]\n",
        "\n",
        "# Create interactive widgets\n",
        "# Dropdown widget for article titles\n",
        "dropdown = widgets.Dropdown(\n",
        "    options=dropdown_options,\n",
        "    description='Article:',\n",
        "    disabled=False\n",
        ")\n",
        "\n",
        "# Create an HTML widget to display the summary with a clickable link\n",
        "summary_html = widgets.HTML(\n",
        "    value='',\n",
        "    placeholder='Summary will appear here...',\n",
        "    description='Summary:',\n",
        "    layout=widgets.Layout(width='100%', height='250px')\n",
        ")\n",
        "\n",
        "# Function to update the summary based on the selected article\n",
        "def update_summary(change):\n",
        "    selected_option = dropdown.value\n",
        "    selected_title = selected_option.split(' - ', 1)[1].split(' (', 1)[0]\n",
        "    selected_row = df_combined[df_combined['title'] == selected_title].iloc[0]\n",
        "    summary = selected_row['summary']\n",
        "    publish_date = selected_row['published_str']\n",
        "    publisher = selected_row['publisher']\n",
        "    article_url = selected_row['url']\n",
        "\n",
        "    # Use HTML to create a clickable link and format the summary text\n",
        "    summary_html.value = (f\"<b>Title:</b> {selected_title}<br>\"\n",
        "                          f\"<b>Published:</b> {publish_date}<br>\"\n",
        "                          f\"<b>Publisher:</b> {publisher}<br>\"\n",
        "                          f\"<b>URL:</b> <a href='{article_url}' target='_blank'>Click here to view the article</a><br><br>\"\n",
        "                          f\"<b>Summary:</b><br>{summary}\")\n",
        "\n",
        "# Link the dropdown widget to the update function\n",
        "dropdown.observe(update_summary, names='value')\n",
        "\n",
        "# Display the widgets\n",
        "display(dropdown, summary_html)\n"
      ],
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      "execution_count": 7,
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          "name": "stderr",
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          "text": [
            "<ipython-input-7-1098fcddd224>:58: DeprecationWarning: The 'Search.results' method is deprecated, use 'Client.results' instead\n",
            "  for result in search.results():\n",
            "Your max_length is set to 150, but your input_length is only 111. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=55)\n"
          ]
        },
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            "text/plain": [
              "Dropdown(description='Article:', options=('2024-06-22 - Bone Fracture Classification using Transfer Learning (…"
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              "HTML(value='', description='Summary:', layout=Layout(height='250px', width='100%'), placeholder='Summary will …"
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