Topic-Aware Evaluation and Transformer Methods for Topic-Controllable Summarization

Topic-controllable summarization is an emerging research area with a wide range of potential applications. However, existing approaches suffer from significant limitations. First, there is currently no established evaluation metric for this task. Furthermore, existing methods built upon recurrent architectures, which can significantly limit their performance compared to more recent Transformer-based architectures, while they also require modifications to the model's architecture for controlling the topic. In this work, we propose a new topic-oriented evaluation measure to automatically evaluate the generated summaries based on the topic affinity between the generated summary and the desired topic. We also conducted a user study that validates the reliability of this measure. Finally, we propose simple, yet powerful methods for topic-controllable summarization either incorporating topic embeddings into the model's architecture or employing control tokens to guide the summary generation. Experimental results show that control tokens can achieve better performance compared to more complicated embedding-based approaches while being at the same time significantly faster.