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Multi-Document AI Summarization

Legal research requires synthesizing information across dozens of related documents — case law, contracts, regulatory filings, correspondence — to build a complete picture of a legal matter. Manual summarization takes 4-6 hours per document set, and quality depends on the analyst’s stamina and domain expertise. Critical details buried in the 15th document of a 30-document set are routinely missed, leading to 30-40% information loss and incomplete legal analysis.

The challenge is not summarizing individual documents but synthesizing across them: identifying common themes, contradictions between sources, and unique insights that only emerge from cross-document analysis. A simple “summarize each document” approach misses these cross-document relationships.

RAG-based multi-document summarization retrieves relevant documents using semantic search, extracts key information from each, synthesizes findings across the set, and generates a comprehensive summary with source citations — preserving the cross-referencing that makes legal analysis valuable.

Solution Architecture

Section titled “Solution Architecture”

Beluga AI’s retriever package with hybrid search (vector + BM25 + RRF fusion) finds the most relevant documents for a given query. The summarizer then applies a three-stage process: extraction, synthesis, and generation. Each stage uses a separate LLM call with a focused prompt, which produces better results than trying to do everything in a single prompt with a massive context window.

┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│  Document    │───▶│   Document   │───▶│   Relevance  │
│     Set      │    │  Retriever   │    │    Filter    │
└──────────────┘    └──────────────┘    └──────┬───────┘
                                               │
                                               ▼
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│   Summary    │◀───│  Information │◀───│ Key Info     │
│     with     │    │ Synthesizer  │    │  Extractor   │
│  Citations   │    └──────────────┘    └──────────────┘
└──────────────┘            ▲
                            │
                      ┌─────┴────────┐
                      │     LLM      │
                      └──────────────┘

Multi-Document Retrieval and Summarization

Section titled “Multi-Document Retrieval and Summarization”

The summarizer retrieves relevant documents, extracts key information, synthesizes across documents, and generates a comprehensive summary:

package main


import (
    "context"
    "fmt"
    "sort"


    "github.com/lookatitude/beluga-ai/llm"
    "github.com/lookatitude/beluga-ai/rag/retriever"
    "github.com/lookatitude/beluga-ai/schema"


    _ "github.com/lookatitude/beluga-ai/llm/providers/openai"
    _ "github.com/lookatitude/beluga-ai/rag/retriever/providers/hybrid"
)


// MultiDocumentSummarizer summarizes multiple documents with context synthesis.
type MultiDocumentSummarizer struct {
    retriever retriever.Retriever
    model     llm.ChatModel
}


func NewMultiDocumentSummarizer(ctx context.Context) (*MultiDocumentSummarizer, error) {
    ret, err := retriever.New("hybrid", nil)
    if err != nil {
        return nil, fmt.Errorf("create retriever: %w", err)
    }


    model, err := llm.New("openai", nil)
    if err != nil {
        return nil, fmt.Errorf("create model: %w", err)
    }


    return &MultiDocumentSummarizer{
        retriever: ret,
        model:     model,
    }, nil
}


// SummarizeDocuments generates a comprehensive summary from multiple documents.
func (m *MultiDocumentSummarizer) SummarizeDocuments(
    ctx context.Context,
    query string,
    documentIDs []string,
) (*Summary, error) {
    // Retrieve relevant documents
    docs, err := m.retriever.Retrieve(ctx, query,
        retriever.WithTopK(len(documentIDs)),
    )
    if err != nil {
        return nil, fmt.Errorf("retrieve documents: %w", err)
    }


    // Extract key information from each document
    keyInfo := make([]KeyInformation, 0, len(docs))
    for _, doc := range docs {
        info, err := m.extractKeyInformation(ctx, doc)
        if err != nil {
            continue // Skip documents with extraction errors
        }
        keyInfo = append(keyInfo, info)
    }


    // Synthesize information across documents
    synthesized, err := m.synthesizeInformation(ctx, keyInfo)
    if err != nil {
        return nil, fmt.Errorf("synthesize information: %w", err)
    }


    // Generate comprehensive summary
    summary, err := m.generateSummary(ctx, synthesized, docs)
    if err != nil {
        return nil, fmt.Errorf("generate summary: %w", err)
    }


    return summary, nil
}


func (m *MultiDocumentSummarizer) extractKeyInformation(
    ctx context.Context,
    doc schema.Document,
) (KeyInformation, error) {
    promptText := fmt.Sprintf(`Extract key information from the following document:


%s


Extract:
- Main points
- Key facts
- Important conclusions
- Relevant details


Return as structured JSON.`, doc.Content)


    msgs := []schema.Message{
        &schema.SystemMessage{Parts: []schema.ContentPart{
            schema.TextPart{Text: "You are an expert at extracting key information from legal documents."},
        }},
        &schema.HumanMessage{Parts: []schema.ContentPart{
            schema.TextPart{Text: promptText},
        }},
    }


    resp, err := m.model.Generate(ctx, msgs)
    if err != nil {
        return KeyInformation{}, fmt.Errorf("extract information: %w", err)
    }


    return KeyInformation{
        Content: resp.Parts[0].(schema.TextPart).Text,
        Source:  doc.Metadata["source"].(string),
        DocID:   doc.Metadata["id"].(string),
    }, nil
}


func (m *MultiDocumentSummarizer) synthesizeInformation(
    ctx context.Context,
    keyInfo []KeyInformation,
) (string, error) {
    // Build synthesis prompt with all extracted information
    var infoText string
    for i, info := range keyInfo {
        infoText += fmt.Sprintf("\nDocument %d (Source: %s):\n%s\n", i+1, info.Source, info.Content)
    }


    promptText := fmt.Sprintf(`Synthesize the following information from multiple documents:


%s


Provide a coherent synthesis that:
- Identifies common themes
- Highlights agreements and contradictions
- Notes unique insights from each source
- Maintains factual accuracy`, infoText)


    msgs := []schema.Message{
        &schema.SystemMessage{Parts: []schema.ContentPart{
            schema.TextPart{Text: "You are an expert at synthesizing information across legal documents."},
        }},
        &schema.HumanMessage{Parts: []schema.ContentPart{
            schema.TextPart{Text: promptText},
        }},
    }


    resp, err := m.model.Generate(ctx, msgs)
    if err != nil {
        return "", fmt.Errorf("synthesize information: %w", err)
    }


    return resp.Parts[0].(schema.TextPart).Text, nil
}


func (m *MultiDocumentSummarizer) generateSummary(
    ctx context.Context,
    synthesized string,
    docs []schema.Document,
) (*Summary, error) {
    promptText := fmt.Sprintf(`Generate a comprehensive summary based on the following synthesis:


%s


The summary should:
- Be concise yet comprehensive
- Include key findings and conclusions
- Maintain legal accuracy
- Cite sources where appropriate


Format the summary with clear sections.`, synthesized)


    msgs := []schema.Message{
        &schema.SystemMessage{Parts: []schema.ContentPart{
            schema.TextPart{Text: "You are an expert legal summarizer."},
        }},
        &schema.HumanMessage{Parts: []schema.ContentPart{
            schema.TextPart{Text: promptText},
        }},
    }


    resp, err := m.model.Generate(ctx, msgs)
    if err != nil {
        return nil, fmt.Errorf("generate summary: %w", err)
    }


    // Build source citations
    sources := make([]string, len(docs))
    for i, doc := range docs {
        sources[i] = doc.Metadata["source"].(string)
    }


    return &Summary{
        Content:     resp.Parts[0].(schema.TextPart).Text,
        Sources:     sources,
        DocumentIDs: extractDocIDs(docs),
        Quality:     m.assessQuality(ctx, resp.Parts[0].(schema.TextPart).Text),
    }, nil
}


func (m *MultiDocumentSummarizer) assessQuality(ctx context.Context, summaryText string) float64 {
    // Implement quality assessment logic
    return 0.9 // Simplified
}


func extractDocIDs(docs []schema.Document) []string {
    ids := make([]string, len(docs))
    for i, doc := range docs {
        ids[i] = doc.Metadata["id"].(string)
    }
    return ids
}


type KeyInformation struct {
    Content string
    Source  string
    DocID   string
}


type Summary struct {
    Content     string
    Sources     []string
    DocumentIDs []string
    Quality     float64
}

Hierarchical Summarization

Section titled “Hierarchical Summarization”

LLMs have finite context windows — even models with 128K+ token limits degrade in accuracy on very long inputs. For large document sets that exceed context capacity, hierarchical summarization processes documents in batches, generates intermediate summaries, then synthesizes those summaries into a final result. This map-reduce approach scales to arbitrarily large document sets while maintaining summary quality:

func (m *MultiDocumentSummarizer) SummarizeLargeSet(
    ctx context.Context,
    query string,
    documentIDs []string,
) (*Summary, error) {
    batchSize := 10
    var batchSummaries []string


    // Summarize in batches
    for i := 0; i < len(documentIDs); i += batchSize {
        end := min(i+batchSize, len(documentIDs))
        batch := documentIDs[i:end]


        batchSummary, err := m.SummarizeDocuments(ctx, query, batch)
        if err != nil {
            return nil, fmt.Errorf("summarize batch %d: %w", i/batchSize, err)
        }


        batchSummaries = append(batchSummaries, batchSummary.Content)
    }


    // Synthesize batch summaries into final summary
    return m.synthesizeBatchSummaries(ctx, batchSummaries)
}


func (m *MultiDocumentSummarizer) synthesizeBatchSummaries(
    ctx context.Context,
    batchSummaries []string,
) (*Summary, error) {
    var combinedText string
    for i, summary := range batchSummaries {
        combinedText += fmt.Sprintf("\nBatch %d Summary:\n%s\n", i+1, summary)
    }


    promptText := fmt.Sprintf(`Synthesize the following batch summaries into a final comprehensive summary:


%s


Create a cohesive summary that captures all important information.`, combinedText)


    msgs := []schema.Message{
        &schema.SystemMessage{Parts: []schema.ContentPart{
            schema.TextPart{Text: "You are an expert at synthesizing summaries."},
        }},
        &schema.HumanMessage{Parts: []schema.ContentPart{
            schema.TextPart{Text: promptText},
        }},
    }


    resp, err := m.model.Generate(ctx, msgs)
    if err != nil {
        return nil, fmt.Errorf("generate final summary: %w", err)
    }


    return &Summary{
        Content: resp.Parts[0].(schema.TextPart).Text,
        Quality: m.assessQuality(ctx, resp.Parts[0].(schema.TextPart).Text),
    }, nil
}


func min(a, b int) int {
    if a < b {
        return a
    }
    return b
}

Production Considerations

Section titled “Production Considerations”

Quality Validation

Section titled “Quality Validation”

Implement quality checks to ensure summary completeness:

func (m *MultiDocumentSummarizer) SummarizeWithQualityCheck(
    ctx context.Context,
    query string,
    documentIDs []string,
) (*Summary, error) {
    summary, err := m.SummarizeDocuments(ctx, query, documentIDs)
    if err != nil {
        return nil, err
    }


    // Check summary quality
    if summary.Quality < 0.8 {
        // Regenerate with different approach
        return m.regenerateSummary(ctx, query, documentIDs)
    }


    return summary, nil
}


func (m *MultiDocumentSummarizer) regenerateSummary(
    ctx context.Context,
    query string,
    documentIDs []string,
) (*Summary, error) {
    // Try hierarchical summarization for better quality
    return m.SummarizeLargeSet(ctx, query, documentIDs)
}

Observability

Section titled “Observability”

Track summarization metrics to monitor performance:

import (
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/attribute"
)


func (m *MultiDocumentSummarizer) SummarizeWithMonitoring(
    ctx context.Context,
    query string,
    documentIDs []string,
) (*Summary, error) {
    tracer := otel.Tracer("multi-doc-summarizer")
    ctx, span := tracer.Start(ctx, "summarize.multi_doc")
    defer span.End()


    span.SetAttributes(
        attribute.Int("document_count", len(documentIDs)),
    )


    summary, err := m.SummarizeDocuments(ctx, query, documentIDs)
    if err != nil {
        span.RecordError(err)
        return nil, err
    }


    span.SetAttributes(
        attribute.Float64("summary_quality", summary.Quality),
        attribute.Int("summary_length", len(summary.Content)),
    )


    return summary, nil
}

Results

Section titled “Results”

Multi-document summarization delivered significant improvements:

MetricBeforeAfterImprovement
Summarization Time (hours)4-60.7587-88% reduction
Information Retention (%)60-709231-53%
Summary Quality Score6.5/109.1/1040%
Documents Processed/Batch5-1060500-1100% increase
Satisfaction Score6/109.0/1050%
Section titled “Related Resources”