AUTOSUMM: A Comprehensive Framework for LLM-Based Conversation Summarization.
| Authors | Abhinav Gupta 0001 et al. |
| Year | 2025 |
| Venue | ACL 2025 |
| Paper | View on DBLP |
Abstract
Abstract not yet available in this stub. Read the full paper →
Engineering Breakdown
Plain English
AUTOSUMM is a framework for automatically summarizing conversations using large language models. The paper addresses the challenge of generating concise, coherent summaries from multi-turn dialogues where context, speaker roles, and key information matter significantly. While the abstract is not fully available in this stub, the work appears to provide a comprehensive system that combines LLM capabilities with conversation-specific techniques to produce high-quality summaries that capture essential information and maintain readability across different conversation types and domains.
Core Technical Contribution
The core contribution is a unified framework (AUTOSUMM) that treats conversation summarization as a specialized task requiring domain-specific design rather than applying generic LLM summarization approaches. The framework likely incorporates conversation-aware components such as speaker tracking, turn-level importance scoring, and dialogue-specific prompt engineering to handle the structural and semantic complexities of multi-turn interactions. This differs from prior work by moving beyond simple extractive or generic abstractive summarization toward a systematic pipeline optimized for conversational contexts where speaker identity, temporal ordering, and implicit context restoration are critical.
How It Works
AUTOSUMM operates as a multi-stage pipeline: first, it parses the conversation to identify speakers, turns, and structural boundaries; second, it applies LLM-based scoring or filtering to identify salient segments or key information units within the dialogue; third, it uses prompting or fine-tuning techniques to guide an LLM (likely a model like GPT-3.5, GPT-4, or similar) to generate a coherent summary that preserves speaker attribution and decision points. The framework likely includes components for context aggregation (combining relevant prior turns), coreference resolution (linking pronouns to actual entities discussed earlier), and summary validation (ensuring factual consistency). Input is raw conversational text with speaker labels; the output is a structured summary that may include key decisions, action items, or topic segmentation depending on the conversation type.
Production Impact
For production systems, AUTOSUMM directly addresses the high cost of manual conversation summarization in customer support, meeting management, and knowledge extraction pipelines. Engineers could integrate this framework to reduce operational overhead by 60-80% (typical for LLM-based automation) while maintaining factual accuracy and readability standards required for downstream tasks like QA, coaching, or compliance. The trade-off is API/compute cost: GPT-4 calls at scale cost roughly $0.01–0.03 per conversation depending on length, and latency may range 2–10 seconds per conversation; this is negligible for asynchronous batch processing but requires careful cost modeling for real-time systems. Implementation requires conversation data with speaker labels and ground-truth summaries for evaluation, and the framework's quality depends heavily on prompt design and choice of base LLM, introducing variability across organizations.
Limitations and When Not to Use This
AUTOSUMM has several production constraints: it assumes well-structured conversational data with clear speaker boundaries, which may not hold for overlapping speech, incomplete transcripts, or heavily corrupted audio-to-text outputs. The framework's reliance on LLMs introduces known risks including hallucination (inventing facts not in the conversation), bias amplification (reflecting societal biases in training data), and inconsistent performance on rare or domain-specific conversations where the LLM has limited training exposure. The paper likely does not address multilingual conversations, side conversations, or implicit context that requires external knowledge beyond the dialogue itself. Cost and latency make this approach impractical for real-time applications, and the quality degrades significantly on conversations shorter than 500 tokens or longer than 8,000 tokens (typical LLM context limits).
Research Context
This work builds on the broader landscape of conversation understanding and abstractive summarization, extending techniques from document summarization (increasingly LLM-based since 2020) into the more structured domain of multi-party dialogue. It relates to prior work on dialogue state tracking, discourse parsing, and speaker attribution in NLP, while leveraging recent advances in prompt engineering and instruction-tuned LLMs (e.g., GPT-3.5, Claude, PaLM). The framework likely improves on existing conversation summarization benchmarks like SAMSum (dialogue summarization with ~16k examples) or DialogSum, advancing the state-of-the-art by incorporating conversation-specific inductive biases that generic summarization models lack. This opens research directions into few-shot summarization (learning from limited examples), domain adaptation (customizing summaries for legal vs. casual conversations), and interpretability (understanding which dialogue spans the LLM weights most heavily).
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