What are some of the current research trends and directions in text summarization?

1 Extractive vs. abstractive methods

One of the main distinctions in text summarization is between extractive and abstractive methods. Extractive methods select the most important sentences or phrases from the original text and concatenate them to form a summary, while abstractive methods generate new sentences that paraphrase or synthesize the main ideas of the text. Extractive methods are simpler and faster, but they may miss relevant information or produce redundant or incoherent summaries. Abstractive methods are more flexible and expressive, but they require more computational resources and may introduce errors or inaccuracies.

3 Pre-training and fine-tuning

A related trend in text summarization is the use of pre-training and fine-tuning techniques, which leverage existing large-scale language models, such as BERT, GPT-2, or T5, that have been trained on massive amounts of unlabeled text data. These models can provide rich and generalizable features and embeddings for text summarization, and can be fine-tuned or adapted to specific domains or tasks with smaller amounts of labeled data. This can improve the performance and efficiency of text summarization models, and reduce the data scarcity and domain adaptation problems. However, these techniques also raise some ethical and social issues, such as the potential for generating biased or misleading summaries, or the difficulty of ensuring the quality and reliability of the pre-trained models.

5 Evaluation and feedback

A final and crucial aspect of text summarization research is the evaluation and feedback of the generated summaries. Evaluation methods can be classified into intrinsic and extrinsic methods. Intrinsic methods measure the quality of the summaries based on some criteria or metrics, such as relevance, coherence, readability, or informativeness. These methods can be further divided into automatic and human methods. Automatic methods compare the summaries with some reference summaries, such as ROUGE or BLEU, or use some neural network models, such as BERTScore or BARTScore. Human methods rely on the judgments or ratings of human evaluators, such as experts or crowd workers. Extrinsic methods measure the usefulness or impact of the summaries for some downstream tasks or applications, such as question answering, sentiment analysis, or decision making. These methods can also involve automatic or human evaluation, depending on the task or application. Feedback methods aim to improve the quality of the summaries by providing some guidance or correction to the text summarization models, such as reinforcement learning, active learning, or interactive learning.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?