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Found 11 papers, 0 papers with code
Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization
This work focuses on the task of query-based meeting summarization in which the summary of a context (meeting transcript) is generated in response to a specific query.
Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?
In this paper, we investigate whether smaller, compact LLMs are a good alternative to the comparatively Larger LLMs2 to address significant costs associated with utilizing LLMs in the real world.
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs
In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models.
Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective
This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs).
AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching
In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise.
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop
Telephone transcription data can be very noisy due to speech recognition errors, disfluencies, etc.
Entity-level Sentiment Analysis in Contact Center Telephone Conversations
Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text.
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts
We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the nature of spoken conversation and artifacts of automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Developing a Production System for Purpose of Call Detection in Business Phone Conversations
In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time.
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base.
Improving Punctuation Restoration for Speech Transcripts via External Data
To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
