ai-summarizing

name

ai-summarizing

description

Condense long content into short summaries using AI. Use when summarizing meeting notes, condensing articles, creating executive briefs, extracting action items, generating TL;DRs, creating digests from long threads, summarizing customer conversations, or turning lengthy documents into bullet points. Also used for AI summary too generic, summarize Slack threads, condense customer feedback, meeting transcript summary, executive summary generator, AI-powered digest, summarize legal documents, TLDR for long emails, abstractive summarization, extractive summary with AI, bullet point summary from long text, summarize research papers, call transcript summary, weekly digest generator, summarize support tickets, AI loses important details when summarizing, key takeaways extraction.

Build an AI Summarizer

Guide the user through building AI that condenses long content into useful summaries. Uses DSPy to produce consistent, faithful summaries with controllable length and detail.

Step 1: Understand the task

Ask the user:

1. What are you summarizing? (meeting transcripts, articles, support threads, documents, emails?)
2. What format should the summary be? (bullet points, narrative paragraph, executive brief, action items?)
3. How long should summaries be? (one sentence, a paragraph, 3-5 bullets, custom word limit?)
4. Who reads the summaries? (executives, team members, customers, developers?)

Step 2: Build a basic summarizer

Simple text-to-summary

import dspy

lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

class Summarize(dspy.Signature):
    """Summarize the text concisely while preserving key information."""
    text: str = dspy.InputField(desc="The text to summarize")
    summary: str = dspy.OutputField(desc="A concise summary of the text")

summarizer = dspy.ChainOfThought(Summarize)
result = summarizer(text="...")
print(result.summary)

Audience-aware summary

Adapt the signature for specific audiences:

class SummarizeForAudience(dspy.Signature):
    """Summarize the text for the target audience."""
    text: str = dspy.InputField(desc="The text to summarize")
    audience: str = dspy.InputField(desc="Who will read this summary")
    summary: str = dspy.OutputField(desc="A summary tailored to the audience")

Step 3: Structured summaries

Extract multiple aspects from the same content at once:

Meeting transcript processor

from pydantic import BaseModel, Field

class MeetingSummary(BaseModel):
    tldr: str = Field(description="One-sentence overview of the meeting")
    decisions: list[str] = Field(description="Decisions that were made")
    action_items: list[str] = Field(description="Tasks assigned with owners if mentioned")
    key_points: list[str] = Field(description="Important facts or updates discussed")

class SummarizeMeeting(dspy.Signature):
    """Extract a structured summary from a meeting transcript."""
    transcript: str = dspy.InputField(desc="Meeting transcript")
    summary: MeetingSummary = dspy.OutputField()

summarizer = dspy.ChainOfThought(SummarizeMeeting)

Parallel multi-aspect extraction

Extract different aspects independently for better quality:

class ExtractDecisions(dspy.Signature):
    """Extract decisions made in this meeting."""
    transcript: str = dspy.InputField()
    decisions: list[str] = dspy.OutputField(desc="Decisions that were made")

class ExtractActionItems(dspy.Signature):
    """Extract action items with assigned owners."""
    transcript: str = dspy.InputField()
    action_items: list[str] = dspy.OutputField(desc="Tasks with owners")

class ExtractKeyFacts(dspy.Signature):
    """Extract key facts and updates discussed."""
    transcript: str = dspy.InputField()
    key_facts: list[str] = dspy.OutputField(desc="Important facts and updates")

class MeetingSummarizer(dspy.Module):
    def __init__(self):
        self.tldr = dspy.ChainOfThought("transcript -> tldr")
        self.decisions = dspy.ChainOfThought(ExtractDecisions)
        self.actions = dspy.ChainOfThought(ExtractActionItems)
        self.facts = dspy.ChainOfThought(ExtractKeyFacts)

    def forward(self, transcript):
        return dspy.Prediction(
            tldr=self.tldr(transcript=transcript).tldr,
            decisions=self.decisions(transcript=transcript).decisions,
            action_items=self.actions(transcript=transcript).action_items,
            key_facts=.facts(transcript=transcript).key_facts,
        )

Step 4: Control length and detail

Word limit enforcement

class SummarizeWithLimit(dspy.Signature):
    """Summarize the text within the word limit."""
    text: str = dspy.InputField()
    max_words: int = dspy.InputField(desc="Maximum number of words for the summary")
    summary: str = dspy.OutputField(desc="A concise summary within the word limit")

class LengthControlledSummarizer(dspy.Module):
    def __init__(self):
        self.summarize = dspy.ChainOfThought(SummarizeWithLimit)

    def forward(self, text, max_words=100):
        return self.summarize(text=text, max_words=max_words)

def length_reward(args, pred):
    """Penalize summaries that exceed the word limit."""
    word_count = len(pred.summary.split())
    max_words = args["max_words"]
    if word_count <= max_words:
        return 1.0
    return max(0.0, 1.0 - (word_count - max_words) / max_words)

# Wrap with Refine to enforce the limit
enforced = dspy.Refine(module=LengthControlledSummarizer(), N=3, reward_fn=length_reward, threshold=0.9)

Detail level control

Use a detail parameter to control how much information to keep:

from typing import Literal

class SummarizeWithDetail(dspy.Signature):
    """Summarize the text at the specified detail level."""
    text: str = dspy.InputField()
    detail_level: Literal["brief", "standard", "detailed"] = dspy.InputField(
        desc="brief = 1-2 sentences, standard = short paragraph, detailed = comprehensive"
    )
    summary: str = dspy.OutputField()

class MultiDetailSummarizer(dspy.Module):
    def __init__(self):
        self.summarize = dspy.ChainOfThought(SummarizeWithDetail)

    def forward(self, text, detail_level="standard"):
        result = self.summarize(text=text, detail_level=detail_level)
        return result

def detail_reward(args, pred):
    """Soft penalty for exceeding detail-level word limits."""
    limits = {"brief": 50, "standard": 150, "detailed": 400}
    max_words = limits[args["detail_level"]]
    word_count = len(pred.summary.split())
    if word_count <= max_words:
        return 1.0
    return max(0.0, 1.0 - 0.5 * (word_count - max_words) / max_words)

Step 5: Handle long documents

When the input is too long for a single LM call, use chunked summarization.

Map-reduce pattern

Split → summarize each chunk → combine:

class SummarizeChunk(dspy.Signature):
    """Summarize this section of a larger document."""
    chunk: str = dspy.InputField(desc="A section of a larger document")
    chunk_summary: str = dspy.OutputField(desc="Key points from this section")

class CombineSummaries(dspy.Signature):
    """Combine section summaries into one coherent summary."""
    section_summaries: list[str] = dspy.InputField(desc="Summaries of each section")
    original_length: int = dspy.InputField(desc="Word count of the original document")
    summary: str = dspy.OutputField(desc="A unified summary of the full document")

class LongDocSummarizer(dspy.Module):
    def __init__(self, chunk_size=2000):
        self.chunk_size = chunk_size
        self.map_step = dspy.ChainOfThought(SummarizeChunk)
        self.reduce_step = dspy.ChainOfThought(CombineSummaries)

    def forward(self, text):
        chunks = self._split(text)

        # Map: summarize each chunk
        chunk_summaries = []
        for chunk in chunks:
            result = self.map_step(chunk=chunk)
            chunk_summaries.append(result.chunk_summary)

        # Reduce: combine into final summary
        return self.reduce_step(
            section_summaries=chunk_summaries,
            original_length=len(text.split()),
        )

     ():
        words = text.split()
        chunks = []
         i  (, (words), .chunk_size):
            chunks.append(.join(words[i:i + .chunk_size]))
         chunks

Hierarchical summarization

For very long documents, summarize chunks, then summarize the summaries:

class HierarchicalSummarizer(dspy.Module):
    def __init__(self, chunk_size=2000, max_chunks_per_level=10):
        self.chunk_size = chunk_size
        self.max_chunks = max_chunks_per_level
        self.summarize_chunk = dspy.ChainOfThought(SummarizeChunk)
        self.combine = dspy.ChainOfThought(CombineSummaries)

    def forward(self, text):
        chunks = self._split(text)
        summaries = [self.summarize_chunk(chunk=c).chunk_summary for c in chunks]

        # If still too many summaries, summarize again
        while len(summaries) > self.max_chunks:
            grouped = [summaries[i:i+self.max_chunks]
                       for i in range(0, len(summaries), self.max_chunks)]
            summaries = [
                self.combine(
                    section_summaries=group,
                    original_length=len(text.split()),
                ).summary
                for group in grouped
            ]

        return self.combine(
            section_summaries=summaries,
            original_length=len(text.split()),
        )

    def _split(self, text):
        words = text.split()
        return [.join(words[i:i+.chunk_size])
                 i  (, (words), .chunk_size)]

Step 6: Multi-format output

Generate different summary formats from the same input:

class FlexibleSummarizer(dspy.Module):
    def __init__(self):
        self.bullets = dspy.ChainOfThought(BulletSummary)
        self.narrative = dspy.ChainOfThought(NarrativeSummary)
        self.executive = dspy.ChainOfThought(ExecutiveBrief)

    def forward(self, text, format="bullets"):
        if format == "bullets":
            return self.bullets(text=text)
        elif format == "narrative":
            return self.narrative(text=text)
        elif format == "executive":
            return self.executive(text=text)

class BulletSummary(dspy.Signature):
    """Summarize as a bulleted list of key points."""
    text: str = dspy.InputField()
    summary: str = dspy.OutputField(desc="Bulleted list of key points")

class NarrativeSummary(dspy.Signature):
    """Summarize as a flowing narrative paragraph."""
    text: str = dspy.InputField()
    summary: str = dspy.OutputField(desc="A narrative paragraph summary")

class ExecutiveBrief(dspy.Signature):
    """Create a brief executive summary with context, key findings, and recommendation."""
    text:  = dspy.InputField()
    context:  = dspy.OutputField(desc=)
    key_findings: [] = dspy.OutputField(desc=)
    recommendation:  = dspy.OutputField(desc=)

Step 7: Test and optimize

Faithfulness metric

Does the summary accurately reflect the source? No fabricated claims?

class JudgeFaithfulness(dspy.Signature):
    """Judge whether the summary is faithful to the source text."""
    source_text: str = dspy.InputField()
    summary: str = dspy.InputField()
    is_faithful: bool = dspy.OutputField(desc="Does the summary only contain info from the source?")
    hallucinated_claims: list[str] = dspy.OutputField(desc="Claims not in the source, if any")

def faithfulness_metric(example, prediction, trace=None):
    judge = dspy.Predict(JudgeFaithfulness)
    result = judge(source_text=example.text, summary=prediction.summary)
    return result.is_faithful

Key-point coverage metric

Does the summary capture the important points?

class JudgeCoverage(dspy.Signature):
    """Judge whether the summary covers the key points."""
    source_text: str = dspy.InputField()
    summary: str = dspy.InputField()
    reference_summary: str = dspy.InputField(desc="Gold-standard summary for comparison")
    coverage_score: float = dspy.OutputField(desc="0.0-1.0 how well key points are covered")

def coverage_metric(example, prediction, trace=None):
    judge = dspy.Predict(JudgeCoverage)
    result = judge(
        source_text=example.text,
        summary=prediction.summary,
        reference_summary=example.summary,
    )
    return result.coverage_score

Combined metric

def summary_metric(example, prediction, trace=None):
    faithful = faithfulness_metric(example, prediction, trace)
    coverage = coverage_metric(example, prediction, trace)
    concise = len(prediction.summary.split()) < len(example.text.split()) * 0.3
    return (faithful * 0.4) + (coverage * 0.4) + (concise * 0.2)

Optimize

optimizer = dspy.BootstrapFewShot(metric=summary_metric, max_bootstrapped_demos=4)
optimized = optimizer.compile(summarizer, trainset=trainset)

Typical improvement: zero-shot ChainOfThought alone scores ~65-75% on a combined faithfulness+coverage metric. After BootstrapFewShot with 4 demos expect ~80-88%, depending on content type and LM. If the metric plateaus below 80%, upgrade to dspy.MIPROv2 (instruction tuning on top of few-shot).

When NOT to build a summarizer

• You need specific fields, not a summary — extracting names, dates, amounts from text is parsing, not summarizing. Use /ai-parsing-data instead.
• You need to answer questions about the content — if the user will ask different questions each time, build a Q&A system with /ai-searching-docs instead of pre-generating summaries.
• The content is already short (under ~500 words) — a single dspy.Predict call is cheaper and faster than a full summarization pipeline. Only build the infrastructure in this skill for content that genuinely needs condensing.

Choosing the right approach

Approach	Input length	LM calls	Best for
Single-pass (ChainOfThought)	Under ~4K words	1	Most use cases — articles, emails, threads
Structured extraction (Pydantic)	Under ~4K words	1	Meetings, support threads — need action items, decisions
Parallel multi-aspect	Under ~4K words	3-4	When extraction quality matters more than cost
Map-reduce	4K-50K words	N chunks + 1	Reports, transcripts — fits in context per chunk
Hierarchical	50K+ words	N chunks + log(N)	Books, legal docs — too many chunks for map-reduce

Gotchas

• Word/sentence limits are suggestions, not guarantees. LMs routinely overshoot length constraints. Wrap with dspy.Refine and a word-counting reward function to enforce hard limits.
• Faithfulness is the number one failure mode. Summaries confidently include facts not in the source. Always evaluate with a faithfulness metric that checks every claim against the source text.
• Map-reduce loses cross-chunk context. Information that spans chunk boundaries gets lost. Use overlapping chunks (50-100 words overlap) or a hierarchical approach for documents where cross-references matter.
• Claude writes vague signature docstrings like "Summarize the text." Always specify the audience and purpose in the docstring (e.g., "Summarize for a technical PM who needs to decide whether to escalate"). Vague instructions produce generic summaries.
• Claude defaults to bullet points even when narrative is requested. If you want flowing prose, say "narrative paragraph" explicitly in the OutputField desc, not just "summary."

Cross-references

Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>

• Extract structured fields instead of summaries — see /ai-parsing-data
• Answer questions about documents — see /ai-searching-docs
• Measure and improve summarizer quality — see /ai-improving-accuracy
• Verify summaries against source text — see /ai-stopping-hallucinations
• DSPy signatures for defining input/output contracts — see /dspy-signatures
• Refine for enforcing length and quality constraints — see /dspy-refine
• ChainOfThought for reasoning-based summarization — see /dspy-chain-of-thought
• Install /ai-do if you do not have it — it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do

Additional resources

• For worked examples (meetings, support threads, long docs), see examples.md
• For DSPy API signatures, parameter tables, and reward function patterns, see reference.md