Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization
Summarization of multi-party dialogues is a critical capability in industry, enhancing knowledge transfer and operational effectiveness across many domains. However, automatically generating high-quality summaries is challenging, as the ideal summary must satisfy a set of complex, multi-faceted requirements. While summarization has received immense attention in research, prior work has primarily utilized static datasets and benchmarks, a condition rare in practical scenarios where requirements inevitably evolve. In this work, we present an industry case study on developing an agentic system to summarize multi-party interactions. We share practical insights spanning the full development lifecycle to guide practitioners in building reliable, adaptable summarization systems, as well as to inform future research, covering: 1) robust methods for evaluation despite evolving requirements and task subjectivity, 2) component-wise optimization enabled by the task decomposition inherent in an agentic architecture, 3) the impact of upstream data bottlenecks, and 4) the realities of vendor lock-in due to the poor transferability of LLM prompts.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Towards a Responsible AI Development Lifecycle: Lessons From Information Security
Legislation and public sentiment throughout the world have promoted fairness metrics, explainability, and interpretability as prescriptions for the responsible development of ethical artificial intelligence systems. Desp…
FairnessDo the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?
A number of learned sparse and dense retrieval approaches have recently been proposed and proven effective in tasks such as passage retrieval and document retrieval. In this paper we analyze with a replicability study if…
Conversational SearchPassage RetrievalRetrievalFree-Flying Crew Cooperative Robots on the ISS: A Joint Review of Astrobee, CIMON, and Int-Ball Operations
Intra-vehicular free-flying robots are anticipated to support various work in human spaceflight while working side-by-side with astronauts. Such example of robots includes NASA's Astrobee, DLR's CIMON, and JAXA's Int-Bal…
CARIS: A Context-Adaptable Robot Interface System for Personalized and Scalable Human-Robot Interaction
The human-robot interaction (HRI) field has traditionally used Wizard-of-Oz (WoZ) controlled robots to explore navigation, conversational dynamics, human-in-the-loop interactions, and more to explore appropriate robot be…
Dialogue manager domain adaptation using Gaussian process reinforcement learning
Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a computer interface can facilitate swift, human…
Domain AdaptationGaussian Processesreinforcement-learningReinforcement Learning+2