Delving into the abstractivity of summaries
- Autores: Vicent Ahuir Esteve
- Directores de la Tesis: Encarna Segarra Soriano (dir. tes.), Lluís Felip Hurtado Oliver (dir. tes.)
- Lectura: En la Universitat Politècnica de València ( España ) en 2025
- Idioma: inglés
- Tribunal Calificador de la Tesis: Manuel Montes y Gómez (presid.), María José Castro Bleda (secret.), David Eduardo Pinto Avendaño (voc.)
- Programa de doctorado: Programa de Doctorado en Informática por la Universitat Politècnica de València
- Materias:
- Enlaces
- Tesis en acceso abierto en: RiuNet
- Resumen
With the exorbitant amount of content continuously generated by today's technological society, to which one has access almost instantaneously, a person can spend excessive time searching and filtering content to perform a task. Therefore, being able to automatically identify, extract, and condense the most relevant information from any document is vital. In Natural Language Processing (NLP) this problem is addressed by the automatic summary generation or summarization field.
Advances in the design of neural network architectures, most notably the Transformers architecture, have significantly increased the capabilities of language models. These improvements have enabled language models to generate more coherent texts. Therefore, in the automatic summarization field, the scientific community has begun to create summarization models that adopt an abstractive strategy. With the abstractive strategy, we obtain summaries where the summary is written by identifying the most relevant information of the document and reinterpreting the text of the original document to obtain a coherent, informative, and concise summary as possible. Therefore, the abstractive summarization strategy brings the models closer to how human beings summarize content.
This work delves into different challenges within the abstractive strategy of summarization, focusing our efforts on the composition or writing process of summaries. The thesis aims to define in more detail what characterizes the perception of abstractivity in summaries and whether there are grades of variation in that perception, from complete extractive summaries to purely abstract summaries. We also explore ways to determine when two summaries are valid for the exact text despite having notably different wording and structure. Additionally, we study the emotions that arise from certain words in summaries, how the emotions vary from the document to the summary, and how automatic summarization models are influenced by this aspect.
