Abstractive Dialogue and Text Summarisation
A comprehensive study comparing effectiveness of static versus dynamic, context-aware embeddings in dialogue summarization.
This research conducted a comparative analysis of abstractive dialogue summarization techniques in Natural Language Processing (NLP). It contrasted the performance of a custom Seq2Seq Encoder-Decoder model, which employed static embeddings, against advanced transformer-based architectures like BART (Bidirectional Auto-Regressive Transformers) and T5 (Text-to-Text Transfer Transformer), both of which utilized dynamic, context-aware embeddings. The primary objective was to explore how different embedding technologies influenced the accuracy and quality of generated dialogue summaries. The results revealed that the custom Seq2Seq model was limited in its ability to capture dialogue subtleties due to the static nature of its embeddings. In contrast, BART and T5 demonstrated superior contextual comprehension and coherence in summarizing dialogue, largely owing to their use of dynamic embeddings.
BART, in particular, stood out for its effectiveness in dialogue summarization, benefiting from its pre-training and fine-tuning capabilities. Its bidirectional context learning and autoregressive generation enabled it to produce more coherent and contextually rich summaries compared to the other models. The study concluded that models with dynamic embeddings, such as BART and T5, offered significant advantages for abstractive summarization tasks, particularly in dialogue processing. These findings provided valuable insights into the role of embedding technologies in NLP and offered practical guidance for model selection in dialogue-based summarization tasks.