Media houses reporting on public figures, often come with their own biases
stemming from their respective worldviews. A characterization of these
underlying patterns helps us in better understanding and interpreting news
stories. For this, we need diverse or subjective summarizations, which may not
be amenable for classifying into predefined class labels. This work proposes a
zero-shot approach for non-extractive or generative characterizations of person
entities from a corpus using GPT-2. We use well-articulated articles from
several well-known news media houses as a corpus to build a sound argument for
this approach. First, we fine-tune a GPT-2 pre-trained language model with a
corpus where specific person entities are characterized. Second, we further
fine-tune this with demonstrations of person entity characterizations, created
from a corpus of programmatically constructed characterizations. This twice
fine-tuned model is primed with manual prompts consisting of entity names that
were not previously encountered in the second fine-tuning, to generate a simple
sentence about the entity. The results were encouraging, when compared against
actual characterizations from the corpus.

媒体机构报道公众人物时，常常受到自身世界观的影响，从而带有个人偏见。本研究提出了一种零 - shot 方法，用 GPT-2 从语料库中生成非抽取式或生成式的人物实体描述，以更好地理解和解释新闻故事。