 Future work  Future work on this model will focus on generating headlines with higher factual consistency regarding the text. Ideas to achieve this goal include: - Use of coreference resolution as additional preprocessing step for making the relations within the text more explicit to the model. - Use of contrastive learning [^contrastive_learning] - Use of different models for different news topics, as different topics seem to be prone to different types of errors, more specialized models may be able to improve performance. - Use of factuality metric models for reranking beam search candidates in the generation step. - Perform analysis of biases included in the model    [^factuality]: Maynez, Joshua, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. “On Faithfulness and Factuality in Abstractive Summarization.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1906–19. Online: Association for Computational Linguistics, 2020. https://doi.org/10.18653/v1/2020.acl-main.173.  [^summac]: Laban, Philippe, Tobias Schnabel, Paul N. Bennett, and Marti A. Hearst. “SummaC: Re-Visiting NLI-Based Models for Inconsistency Detection in Summarization.” Transactions of the Association for Computational Linguistics 10 (February 9, 2022): 163–77. https://doi.org/10.1162/tacl_a_00453.   Code: https://github.com/tingofurro/summac  [^qafacteval]: Fabbri, Alexander R., Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. “QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization.” arXiv, April 29, 2022. https://doi.org/10.48550/arXiv.2112.08542.   Code: https://github.com/salesforce/QAFactEval  [^dae]: Goyal, Tanya, and Greg Durrett. “Annotating and Modeling Fine-Grained Factuality in Summarization.” arXiv, April 9, 2021. http://arxiv.org/abs/2104.04302.   Code: https://github.com/tagoyal/factuality-datasets  [^contrastive_learning]: Cao, Shuyang, and Lu Wang. “CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization.” In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 6633–49. Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, 2021. https://doi.org/10.18653/v1/2021.emnlp-main.532.   