EOS: Controllable Entity-Oriented Summarization

Abstract

Even though the text summarization task has always been a vital part of Natural Language Processing (NLP) field controlled summarization is still a novel topic Based on different customization purposes we classified controlled summarization into five categories: length-constrained aspect-oriented entity-centric remainder and source-style In this thesis we propose a new Entity-Oriented Summarization (EOS) task combining the appeals of aspect-oriented and entity-centric hoping to produce an entity-related summary When conducting EOS task current methods may face three difficulties: lack of aspect/entity-aware summary dataset underperforming word embeddings of infrequent entities and unrelated entity input To handle the first issue previous works define heuristic rules to generate data while we don't need such dataset which makes our method more flexible As for the rare entities we purpose a relation calculation method based on Pointwise Mutual information (PMI) Last we adopt a training skill weight annealing to produce a generic summary even in the situations that the desired entity and article are irrelevant Experiment results show that our model can generate entity-related summaries As for the infrequent and unrelated entities we analyze the influences brought by our proposed solutions and the results show that our model indeed mitigates the issues

Date of Award	2021
Original language	English
Supervisor	Hung-Yu Kao (Supervisor)