# Incorporating Multi-Hypotheses as Soft-Templates in Neural Headline Generation
## 1 Introduction  
The past several years have witnessed the rapid development of end-to-end neural headline generation (NHG) [1; 2; 3; 4; 5; 6]. Through a vast neural network, the end-to-end NHG system maps between the input article and the headline and generates the corresponding headline for a document word by word without additional linguistic knowledge and more manual annotation. Recently, the emergence of large-scale pre-trained language models has further improved the performance of NHG models [7; 8; 9; 10; 11; 12]. Nevertheless, the input information of most previous models is simply the source article. Due to the complexity and verbosity of natural language text, and the limited size of training data, these models tend to deteriorate as they generate more words, often producing irrelevant and repeated words [3; 13]. This deterioration occurs because the models struggle to distinguish important information from noise. The accumulation of word generation further exacerbates this issue. In the preliminary phases of summarization, the template-based approach [14] demonstrates considerable potential. In this approach, researchers create a heuristic template framework by manually formulating rules and filling in the required information, to generate a headline based on these templates. This approach offers the advantage of producing concise and coherent summaries without the need for training data. However, the manual creation of all templates is impractical due to the substantial domain knowledge and labor-intensive nature of the task. [PERSON] et al. [15] and [PERSON] et al. [4] extended this idea in the deep learning context. They proposed to make better use of the available training data and retrieve existing headlines as soft templates to guide the summarization process. Although previous template-based methods avoid the problem of manually designing the headline templates, they had two drawbacks. First, their retrieval modules still need additional retrieval tools and carefully designed retrieving rules. Second, the retrieved headline is uncertain to be semantically consistent with the source article. In Figure 1, we provide a news fragment with a reference headline and different templates. The concise, human-written template to conclude the given article is designated as the \"Hard Template\". The existing headline retrieved from the training data is denoted as the \"Retrieved Headline\" (This example is cited from [PERSON] et al. (2015)). Despite being written by humans and being grammatically correct, the ground truth \"Retrieved Headline\" is semantically inconsistent with the original article. To address these issues, we propose to utilize the multiple hypotheses generated based on a well-trained NHG model as soft templates to improve headline generation performance. The presence of uncertainties in the multiple hypotheses stems from both the confidence of the model and the potential ambiguity arising from linguistic variations ([PERSON], 2016), offering an opportunity to enhance machine translation performance ([PERSON], 2016; [PERSON], 2016). Model uncertainty typically gives rise to the K-best hypotheses obtained through a beam search ([PERSON], 2016). In Figure 1, we also provide hypotheses generated based on a well-trained NHG model which are indicated as Hypothesis 1-4. The hypotheses generated using various sampling methods are expected to have differing headline words while striving to maintain consistent semantics. To this end, we introduce a new model called the **M**ultiple-**H**ypotheses-based NHG (MH-NHG) model. Our approach leverages multiple hypotheses generated by a well-trained basic NHG model, which serve as soft templates to enhance the performance of the NHG model. The MH-NHG model is a hierarchical multi-stage architecture consisting of four layers to effectively integrate the multiple hypotheses.