To bridge the performance gap between systems using ASR transcriptions and human-written synopses, several techniques can be explored that do not depend on high-quality manual annotations. One promising approach is the use of semi-supervised learning, where a small amount of labeled data (e.g., high-quality synopses) is combined with a larger set of unlabeled ASR transcriptions. This can help the model learn to generate more accurate summaries or embeddings by leveraging the structure and patterns found in the labeled data.
Another technique is the implementation of advanced natural language processing (NLP) methods, such as transformer-based models, to refine ASR outputs. These models can be trained to correct common transcription errors and enhance the coherence and relevance of the generated text. Additionally, employing data augmentation strategies, such as paraphrasing or back-translation, can create variations of ASR transcriptions that mimic the richness of human-written content.
Furthermore, utilizing unsupervised summarization techniques can help distill key information from ASR transcriptions, producing concise summaries that retain essential context. By integrating these methods, it is possible to enhance the quality of ASR outputs and reduce the performance gap with human-written synopses, ultimately improving the overall effectiveness of the retrieval system.