Controllable summarization models are typically limited only to a short text, such as a topic mention, a keyword, or an entity, to control the output summary. At the same time, existing models for controllable summarization are prone to generate artificial content, resulting in unreliable summaries. In this work, we propose a method for controllable abstractive summarization that can exploit arbitrary textual context from a short text to a collection of documents to direct the focus of the generated summary. The proposed method incorporates a sentence BERT model to extract an embedding-based representation of the given context, which is then used to tag the most representative words of the input document towards this context. In addition, we propose an unsupervised metric to evaluate the faithfulness of the topic-oriented sentences of the generated summaries with respect to the input document. Experimental results under different zero-shot setups demonstrate that the proposed method surpasses both state-of-the-art large language models (LLMs) and controllable summarization methods. The generated summaries are both reliable and relevant with respect to the input document.

Abstractive summarization is an approach to document summarization that is not limited to selecting sentences from the document but can generate new sentences as well. We address the two main challenges in abstractive summarization: how to evaluate the performance of a summarization model and what is a good training objective. We first introduce new evaluation measures based on the semantic similarity of the input and corresponding summary. The similarity scores are obtained by the fine-tuned BERTurk model using either the cross-encoder or a bi-encoder architecture. The fine-tuning is done on the Turkish Natural Language Inference and Semantic Textual Similarity benchmark datasets. We show that these measures have better correlations with human evaluations compared to Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores and BERTScore. We then introduce a deep reinforcement learning algorithm that uses the proposed semantic similarity measures as rewards, together with a mixed training objective, in order to generate more natural summaries in terms of human readability. We show that training with a mixed training objective function compared to only the maximum-likelihood objective improves similarity scores.

Convolutional sequence to sequence (CNN seq2seq) models have met success in abstractive summarization. However, their outputs often contain repetitive word sequences and logical inconsistencies, limiting the practicality of their application. In this paper, we find the reasons behind the repetition problem in CNN-based abstractive summarization through observing the attention map between the summaries with repetition and their corresponding source documents and mitigate the repetition problem. We propose to reduce the repetition in summaries by attention filter mechanism (ATTF) and sentence-level backtracking decoder (SBD), which dynamically redistributes attention over the input sequence as the output sentences are generated. The ATTF can record previously attended locations in the source document directly and prevent the decoder from attending to these locations. The SBD prevents the decoder from generating similar sentences more than once via backtracking at test. The proposed model outperforms the baselines in terms of ROUGE score, repeatedness, and readability. The results show that this approach generates high-quality summaries with minimal repetition and makes the reading experience better.