Traceable FGD Summarization for Effective Analysis & Deep Dives


Client, an FMCG major, partners with market research agencies to conduct and analyse focus group discussions to better understand consumer behaviour and expectations. The outcome of these studies is often a summary report which captures the major themes/observations as obtained from the FGDs but doesn’t provide a mechanism to correlate insights with the raw conversations of the FGD participants. To achieve the same, the client either needs to peruse the raw transcript of the FGD or generate a coherent summary from the transcript, both of which are manual, time-consuming and expensive. Consequently, the client partnered with Inxite Out to create a solution that could enable them to correlate the insights from research agencies with the raw information extracted from FGDs autonomously and inexpensively.


An end-to-end solution was developed leveraging MEGHNAD, Inxite Out’s Conversation Intelligence Framework. The solution consisted of the following steps:


FGD Summarization: End-to-end process using Meghnad


MEGHNAD’s transcription engine was used to generate transcribed text files from multi-lingual FGD discussions with a typical duration of 1 – 2 hours. The transcribed text files generated had the following elements making them amenable for downstream processing:

    • Speaker-Segmentation: Clearly demarcates the utterances of the moderator of the FGD from the participants.
    • Captures granular time stamps with the utterances to enable lineage tracking.
    • Transcribed text files are all in English, even though the audio files had a mixture of English and Hindi.

Chunking & Summarization

Leveraged MEGHNAD’s Summarization engine to generate a topical and coherent summary of the FGD. It was achieved in the following steps:

    • Chunking of transcribed text files based on temporal information and topic content.
    • LangChain-based map-reduce prompt execution strategy on those chunked texts stored in a vector database post recursive splitting to align with token lengths allowed by large language models used underneath.
    • Summarization of each independent chunk and intelligent aggregation to ensure a coherent summary across chunks.

Incorporation of Lineage Tracking

Facilitated lineage tracking to enable deep dives from the summary to the raw transcript for granular conversation understanding.


  • Focus group discussion summarization instituted at the client to facilitate cross-referencing of market research findings with raw customer conversations.
  • 100% FGD summarization coverage as compared to 20% coverage with legacy methods.
  • 75%+ reduction in FGD summarization cycle time as compared to legacy methods

Case Studies