Papers Conversation Summarization
“Conversation Summarization” 태그가 달린 논문 32편 · 필터 해제
ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach
In this paper, we present ConvoGen: an innovative framework for generating synthetic conversational data using multi-agent systems. Our method leverages few-shot learning and introduces iterative sampling from a dynamica…
Conversation SummarizationFew-Shot Learningintent-classificationIntent ClassificationSafeSpeech: A Comprehensive and Interactive Tool for Analysing Sexist and Abusive Language in Conversations
Detecting toxic language including sexism, harassment and abusive behaviour, remains a critical challenge, particularly in its subtle and context-dependent forms. Existing approaches largely focus on isolated message-lev…
Abusive LanguageConversation SummarizationAfrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond
Speech technologies are transforming interactions across various sectors, from healthcare to call centers and robots, yet their performance on African-accented conversations remains underexplored. We introduce Afrispeech…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Conversation Summarizationspeaker-diarization+3Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History
Translating conversational text, particularly in customer support contexts, presents unique challenges due to its informal and unstructured nature. We propose a context-aware LLM translation system that leverages convers…
Conversation SummarizationTranslationTwo eyes, Two views, and finally, One summary! Towards Multi-modal Multi-tasking Knowledge-Infused Medical Dialogue Summarization
We often summarize a multi-party conversation in two stages: chunking with homogeneous units and summarizing the chunks. Thus, we hypothesize that there exists a correlation between homogeneous speaker chunking and overa…
ChunkingConversation Summarizationdialogue summaryReal-time Speech Summarization for Medical Conversations
In doctor-patient conversations, identifying medically relevant information is crucial, posing the need for conversation summarization. In this work, we propose the first deployable real-time speech summarization system …
Conversation SummarizationText SummarizationExploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes
Summarizing medical conversations poses unique challenges due to the specialized domain and the difficulty of collecting in-domain training data. In this study, we investigate the performance of state-of-the-art doctor-p…
Conversation SummarizationLanguage ModelingLanguage ModellingEffective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries
Few-shot dialogue state tracking (DST) with Large Language Models (LLM) relies on an effective and efficient conversation retriever to find similar in-context examples for prompt learning. Previous works use raw dialogue…
Conversation SummarizationDialogue State TrackingPrompt LearningRetrievalExperience and Evidence are the eyes of an excellent summarizer! Towards Knowledge Infused Multi-modal Clinical Conversation Summarization
With the advancement of telemedicine, both researchers and medical practitioners are working hand-in-hand to develop various techniques to automate various medical operations, such as diagnosis report generation. In this…
Conversation SummarizationRevisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Most existing cross-lingual summarization (CLS) work constructs CLS corpora by simply and directly translating pre-annotated summaries from one language to another, which can contain errors from both summarization and tr…
Conversation SummarizationUniMC: A Unified Framework for Long-Term Memory Conversation via Relevance Representation Learning
Open-domain long-term memory conversation can establish long-term intimacy with humans, and the key is the ability to understand and memorize long-term dialogue history information. Existing works integrate multiple mode…
Conversation SummarizationDecoderRepresentation LearningRetrievalIUTEAM1 at MEDIQA-Chat 2023: Is simple fine tuning effective for multilayer summarization of clinical conversations?
Clinical conversation summarization has become an important application of Natural language Processing. In this work, we intend to analyze summarization model ensembling approaches, that can be utilized to improve the ov…
Conversation SummarizationGenerating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models
A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference for the patient. An effective summary is …
Conversation SummarizationDecision MakingDialogue UnderstandingDiscourse over Discourse: The Need for an Expanded Pragmatic Focus in Conversational AI
The summarization of conversation, that is, discourse over discourse, elevates pragmatic considerations as a pervasive limitation of both summarization and other applications of contemporary conversational AI. Building o…
Conversation SummarizationDERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
Large language models (LLMs) have emerged as valuable tools for many natural language understanding tasks. In safety-critical applications such as healthcare, the utility of these models is governed by their ability to g…
Conversation SummarizationLanguage ModelingLanguage ModellingLarge Language Model+3Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences
Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries. In this paper, we show that factual inconsiste…
Abstractive Text SummarizationConversation SummarizationThe Cross-lingual Conversation Summarization Challenge
We propose the shared task of cross-lingual conversation summarization, \emph{ConvSumX Challenge}, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine transl…
Abstractive Dialogue SummarizationConversation SummarizationCross-Lingual Abstractive SummarizationMachine Translation+2Findings on Conversation Disentanglement
Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as conversational question answering and conve…
Conversational Question AnsweringConversation DisentanglementConversation SummarizationDisentanglement+2Compositional Data Augmentation for Abstractive Conversation Summarization
Recent abstractive conversation summarization systems generally rely on large-scale annotated summaries. However, collecting conversations and annotating their corresponding summaries can be time-consuming and labor-inte…
Conversation SummarizationData AugmentationKnowledge DistillationAdding more data does not always help: A study in medical conversation summarization with PEGASUS
Medical conversation summarization is integral in capturing information gathered during interactions between patients and physicians. Summarized conversations are used to facilitate patient hand-offs between physicians, …
Active LearningConversation SummarizationTransfer Learning