Qiyi Wei’s research while affiliated with Communication University of China and other places

Framing theory is a widely accepted theoretical framework in the field of news communication studies, frequently employed to analyze the content of news reports. This paper innovatively introduces framing theory into the text summarization task and proposes a news text summarization method based on framing theory to address the global context of rapidly increasing speed and scale of information dissemination. Traditional text summarization methods often overlook the implicit deep-level semantic content and situational frames in news texts, and the method proposed in this paper aims to fill this gap. Our deep learning-based news frame identification module can automatically identify frame elements in the text and predict the dominant frame of the text. The frame-aware summarization generation model (FrameSum) can incorporate the identified frame feature into the text representation and attention mechanism, ensuring that the generated summary focuses on the core content of the news report while maintaining high information coverage, readability, and objectivity. Through empirical studies on the standard CNN/Daily Mail dataset, we found that this method performs significantly better in improving summary quality and maintaining the accuracy of news facts.

In a multi-document summarization task, if the user can decide on the summary topic, the generated summary can better align with the reader’s specific needs and preferences. This paper addresses the issue of overly general content generation by common multi-document summarization models and proposes a topic-oriented multi-document summarization (TOMDS) approach. The method is divided into two stages: extraction and abstraction. During the extractive stage, it primarily identifies and retrieves paragraphs relevant to the designated topic, subsequently sorting them based on their relevance to the topic and forming an initial subset of documents. In the abstractive stage, building upon the transformer architecture, the process includes two parts: encoding and decoding. In the encoding part, we integrated an external discourse parsing module that focuses on both micro-level within-paragraph semantic relationships and macro-level inter-paragraph connections, effectively combining these with the implicit relationships in the source document to produce more enriched semantic features. In the decoding part, we incorporated a topic-aware attention mechanism that dynamically zeroes in on information pertinent to the chosen topic, thus guiding the summary generation process more effectively. The proposed model was primarily evaluated using the standard text summary dataset WikiSum. The experimental results show that our model significantly enhanced the thematic relevance and flexibility of the summaries and improved the accuracy of grammatical and semantic comprehension in the generated summaries.

This paper proposes a two-stage automatic text summarization method based on discourse structure, aiming to improve the accuracy and coherence of the summary. In the extractive stage, a text encoder divides the long text into elementary discourse units (EDUs). Then a parse tree based on rhetorical structure theory is constructed for the whole discourse while annotating nuclearity information. The nuclearity terminal nodes are selected based on the summary length requirement, and the key EDU sequence is output. The authors use a pointer generator network and a coverage mechanism in the generation stage. The nuclearity information of EDUs is to update the word attention distribution in the pointer generator, which not only accurately reproduces the critical details of the text but also avoids self-repetition. Experiments on the standard text summarization dataset (CNN/DailyMail) show that the ROUGE score of the proposed two-stage model is better than that of the current best baseline model, and the summary achieves corresponding improvements in accuracy and coherence.