Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization
In this paper, we focus on improving the quality of the summary generated by neural abstractive dialogue summarization systems. Even though pre-trained language models generate well-constructed and promising results, it is still challenging to summarize the conversation of multiple participants since the summary should include a description of the overall situation and the actions of each speaker. This paper proposes self-supervised strategies for speaker-focused post-correction in abstractive dialogue summarization. Specifically, our model first discriminates which type of speaker correction is required in a draft summary and then generates a revised summary according to the required type. Experimental results show that our proposed method adequately corrects the draft summaries, and the revised summaries are significantly improved in both quantitative and qualitative evaluations.
Code (0)
등록된 구현이 없습니다.
Tasks
Abstractive Dialogue SummarizationSimilar Papers 제목 키워드 기반
Behavioral Analysis of Pathological Speaker Embeddings of Patients During Oncological Treatment of Oral Cancer
In this paper, we analyze the behavior of speaker embeddings of patients during oral cancer treatment. First, we found that pre- and post-treatment speaker embeddings differ significantly, notifying a substantial change …
Speaker VerificationBuilding a synchronous corpus of acoustic and 3D facial marker data for adaptive audio-visual speech synthesis
We have created a synchronous corpus of acoustic and 3D facial marker data from multiple speakers for adaptive audio-visual text-to-speech synthesis. The corpus contains data from one female and two male speakers and amo…
Audio-Visual Speech RecognitionSpeech RecognitionSpeech Synthesistext-to-speech+3Meta-Learning Framework for End-to-End Imposter Identification in Unseen Speaker Recognition
Speaker identification systems are deployed in diverse environments, often different from the lab conditions on which they are trained and tested. In this paper, first, we show the problem of generalization using fixed t…
Meta-LearningSpeaker IdentificationSpeaker RecognitionSpeaker VerificationIncorporating speaker embedding and post-filter network for improving speaker similarity of personalized speech synthesis system
In recent years, speech synthesis system can generate speech with high speech quality. However, multi-speaker text-to-speech (TTS) system still require large amount of speech data for each target speaker. In this study, …
Speaker VerificationSpeech Synthesistext-to-speechText to Speech+1Accumulating Word Representations in Multi-level Context Integration for ERC Task
Emotion Recognition in Conversations (ERC) has attracted augmented interest recently because of its pronounced adaptability, which is to forecast the sentiment label for each utterance given a conversation as context. In…
Emotion RecognitionEmotion Recognition in ConversationSentence