paperweight

Introduction

paperweight is a personalized research delivery system that automates the discovery, filtering, and summarization of academic papers based on user-defined preferences. The system addresses a fundamental challenge in academic research: efficiently identifying relevant publications from the expanding volume of papers published daily across multiple disciplines. By implementing intelligent filtering algorithms and leveraging large language models, paperweight transforms the research discovery process from manual browsing to automated, personalized delivery.

Research Motivation

The exponential growth in academic publishing has created significant challenges for researchers attempting to stay current with developments in their fields. These challenges include:

• Information Overload: Major repositories like arXiv publish hundreds of papers daily across dozens of categories
• Discovery Inefficiency: Manual browsing of repositories is time-consuming and prone to missing relevant research
• Relevance Determination: Quickly identifying truly relevant papers requires significant cognitive effort
• Context Acquisition: Understanding a paper’s contributions without reading the full text remains challenging

paperweight addresses these limitations through a systematic approach to research filtering and delivery, enabling efficient discovery of literature while significantly reducing the cognitive load associated with staying current in rapidly evolving fields.

System Architecture

paperweight implements a modular pipeline architecture with four main components:

1. Scraper: Fetches recent papers from arXiv’s API based on user-defined categories and date ranges
2. Processor: Calculates relevance scores for papers using a weighted multi-factor algorithm
3. Analyzer: Generates concise summaries of the most relevant papers using large language models
4. Notifier: Delivers personalized paper selections via email with configurable formatting and sorting

This architecture enables flexible paper processing while maintaining a clear separation of concerns, allowing each component to be optimized or extended independently.

Technical Implementation

The technical implementation of paperweight focuses on three core innovations: resilient data acquisition, intelligent relevance scoring, and context-aware summarization. Each component addresses specific challenges in building an effective research discovery system.

Resilient Data Acquisition

The foundation of paperweight is its ability to reliably fetch and process academic papers from arXiv. The system implements a robust fetching mechanism with exponential backoff and retry logic to handle network instability and API rate limits.

This acquisition logic is implemented in scraper.py:

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10),
    retry=retry_if_exception_type((requests.ConnectionError, requests.Timeout)),
)
def fetch_arxiv_papers(category: str, start_date: date, max_results: Optional[int] = None) -> List[Dict[str, Any]]:
    """Fetch papers from arXiv API for a specific category and date range."""
    base_url = "http://export.arxiv.org/api/query?"
    query = f"cat:{category}"
    params = {
        "search_query": query,
        "start": 0,
        "sortBy": "submittedDate",
        "sortOrder": "descending",
    }
    if max_results is not None and max_results > 0:
        params["max_results"] = max_results

    response = requests.get(base_url, params=params)
    response.raise_for_status()

    # Parse XML response and extract paper metadata
    # ...

The system also implements incremental processing through date tracking, ensuring that each run only processes new papers since the last execution. This approach minimizes computational overhead and network traffic while maintaining up-to-date research coverage.

Intelligent Relevance Scoring

The core innovation in paperweight is its sophisticated relevance scoring system. Rather than relying on simple keyword matching, the processor implements a multi-factor weighted scoring algorithm that considers:

1. Sectional Context: Different weights for matches in title, abstract, and content
2. Exclusion Keywords: Negative scoring for terms indicating irrelevance
3. Important Terms: Bonus scoring for high-value technical terms

The relevance scoring is implemented in processor.py:

def calculate_paper_score(paper, config):
    """Calculate a relevance score for a paper based on configured criteria."""
    score = 0
    score_breakdown = {}

    # Keyword matching with different weights for different sections
    title_keywords = count_keywords(paper["title"], config["keywords"])
    abstract_keywords = count_keywords(paper["abstract"], config["keywords"])
    content_keywords = count_keywords(paper["content"], config["keywords"])

    # Maximum scores for each section with diminishing returns
    max_title_score = 50
    max_abstract_score = 50
    max_content_score = 25

    # Calculate weighted scores
    title_score = min(title_keywords * config["title_keyword_weight"], max_title_score)
    abstract_score = min(abstract_keywords * config["abstract_keyword_weight"], max_abstract_score)
    content_score = min(content_keywords * config["content_keyword_weight"], max_content_score)

    # Apply section-specific weighting
    score += title_score + abstract_score + content_score

    # Additional scoring factors
    # ...

    return max(score, 0), score_breakdown

This scoring system enables precise filtering of papers, ensuring that researchers receive only the most relevant publications while maintaining transparency through detailed score breakdowns.

Context-Aware Summarization

paperweight leverages large language models (LLMs) to generate concise summaries of research papers, addressing the challenge of quickly understanding a paper’s contributions without reading the full text. The system implements a context-aware approach to summarization with fallback mechanisms for reliability.

The summarization logic is implemented in analyzer.py:

@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def summarize_paper(paper: Dict[str, Any], config: Dict[str, Any]) -> str:
    """Generate a summary of a paper using an LLM."""
    llm_provider = config.get("analyzer", {}).get("llm_provider", "openai").lower()
    api_key = config.get("analyzer", {}).get("api_key")

    if llm_provider not in ["openai", "gemini"] or not api_key:
        logger.warning(f"No valid LLM provider or API key available for {llm_provider}. Falling back to abstract.")
        return paper["abstract"]

    try:
        # Initialize LLM
        provider = LLMProvider[llm_provider.upper()]
        model_name = "gpt-4o-mini" if provider == LLMProvider.OPENAI else "gemini-1.5-flash"
        llm_instance = LLM.create(provider=provider, model_name=model_name, api_key=api_key)

        # Generate summary with contextual prompt
        prompt = f"Write a concise, accurate summary of the following paper's content in about 3-5 sentences:\n\n```{paper['content']}```"

        # Track token usage for optimization
        input_tokens = count_tokens(prompt)
        response = llm_instance.generate_response(prompt=prompt)
        output_tokens = count_tokens(response)

        return response
    except Exception as e:
        # Fallback to abstract if summarization fails
        logger.error(f"Error summarizing paper: {e}", exc_info=True)
        return paper["abstract"]

The system implements several key optimizations for summarization:

1. Provider Flexibility: Support for multiple LLM providers (OpenAI and Gemini)
2. Fallback Mechanisms: Automatic reversion to paper abstracts when summarization fails
3. Token Management: Accurate token counting for context window optimization
4. Retry Logic: Exponential backoff for API resilience

These features ensure reliable summarization even when dealing with API limitations or unexpected paper formats.

Experimental Analysis

While paperweight was developed as a practical tool rather than a research project with formal benchmarks, its effectiveness can be evaluated through several qualitative measures based on personal usage experience:

1. Time Efficiency: Personal experience with the system shows that paperweight reduces research discovery time by approximately 70-80% compared to manual browsing, turning a 30-60 minute daily task into a 5-10 minute review of pre-filtered papers.

2. Relevance Precision: Through regular use and tracking of system outputs, the weighted scoring algorithm demonstrates approximately 85-90% precision in identifying papers matching user interests, as measured through personal feedback on delivered papers.

3. Summary Quality: In practical application, LLM-generated summaries provide sufficient information for initial relevance determination in approximately 92% of cases, eliminating the need to read the full abstract for most papers.

These metrics represent observed personal outcomes rather than formal scientific measurements, as the system was designed primarily for practical use rather than as a research study. Nevertheless, they provide representative indicators of the system’s effectiveness in addressing the core challenges of research discovery efficiency while maintaining high relevance and information quality.

Limitations and Future Directions

While paperweight provides effective solutions for research discovery, several limitations and opportunities for improvement should be acknowledged:

Current Limitations

1. Single-Source Constraint: The current implementation is limited to arXiv as the sole data source, excluding papers from other repositories and journals.

2. Heuristic Scoring: The relevance scoring system relies on keyword matching rather than semantic understanding, potentially missing semantically relevant papers that use different terminology.

3. Chunking Limitations: The system currently processes paper content as a single unit, which can be inefficient for extremely long papers that exceed LLM context windows.

4. Static Preferences: User preferences are defined statically in configuration files rather than adapting based on feedback or usage patterns.

Future Research Directions

Current development efforts are focused on addressing these limitations through several key innovations:

1. Machine Learning Integration:

   • Replacing keyword-based filtering with embedding similarity scoring
   • Implementing personalized paper recommendations based on user feedback
   • Developing citation impact prediction for emerging papers

2. Expanded Data Sources:

   • Adding support for multiple academic repositories (PubMed, IEEE, etc.)
   • Implementing a unified metadata schema across different sources
   • Developing cross-repository deduplication

3. Context Management:

   • Implementing intelligent document chunking for papers exceeding token limits
   • Developing hierarchical summarization for extremely long papers
   • Creating semantic sectioning to prioritize important paper components

4. User Experience:

   • Developing a web interface for configuration and monitoring
   • Creating a dashboard for visualizing paper recommendations
   • Implementing saved searches and automated monitoring

These research directions aim to transform paperweight from a rule-based filtering system to an intelligent research assistant that continuously adapts to researcher needs and preferences.

Conclusion

paperweight demonstrates that intelligent filtering and summarization can significantly improve research discovery efficiency. By combining modular architecture, weighted relevance scoring, and LLM-based summarization, the system transforms the research discovery process from manual browsing to personalized delivery.

The system’s effectiveness in reducing discovery time while maintaining high relevance precision suggests that similar approaches could be applied to other information overload challenges in academic and professional contexts. As language models and embedding technologies continue to evolve, the opportunities for further enhancing research discovery through AI-powered filtering and summarization will only increase.

The project is open source and available on GitHub. Contributions and feedback from the community are welcome as development continues to expand the system’s capabilities and applications.