NGEP : A Graph-based Event Planning Framework for Story Generation (2022)

Abstract: To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior works mostly employ end-to-end neural generation models to predict event sequences for a story. However, such generation models struggle to guarantee the narrative coherence of separate events due to the hallucination problem, and additionally the generated event sequences are often hard to control due to the end-to-end nature of the models. To address these challenges, we propose NGEP, an novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. We conduct a range of experiments on multiple criteria, and the results demonstrate that our graph-based neural framework outperforms the state-of-the-art (SOTA) event planning approaches, considering both the performance of event sequence generation and the effectiveness on the downstream task of story generation.

## introduction current neural generation models struggle to generate long stories as it is difficult to guarantee the logical coherence of generated sentences when conditioning only on a limited size input (e.g. leading context or title). therefore, current story generation frameworks are usually split into two stages, planning and writing, using an automatically planned storyline (aka. event sequence) @xcite as the intermediate between planning and writing. in order to plan an event sequence, prior works @xcite @xcite @xcite mostly focus on leveraging end-to-end neural generation models, such as bart @xcite , the event graph g is automatically constructed from the training set, and the potential event candidates are generated according to the conditional probability distribution modelled on g when event planning. if there are no proper candidates for the next event, we leverage a bart-based neural advisor to predict the best choice. 2020), to predict events. however, whilst some efforts @xcite have been made to improve neural event planning (e.g., @xcite use rescoring models to guide the planning process), event planning based on neural generation models still tends to suffer from common limitations: (i) the selection of individual events in the sequence is hard to control (because of the end-to-end generation) @xcite ; and (ii) due to the hallucination problem @xcite @xcite each predicted event is not guaranteed to be complete and accurate. in this study, we propose ngep, a novel neural graph-based event planning framework to predict event sequences for story generation. an overview of the proposed framework is illustrated in figure 1 . firstly, events are extracted from the training set in order to construct an event graph which records the events and their neighbour relations. this graph can then be used at test-time to predict events from a leading context. the conditional probability distribution is modelled by a coherence score calcu-lated with the degrees of event nodes and the concurrency of predicted events. when an event graph is unable to generate event candidates, i.e. no edges point to another event, a bart-based neural advisor is introduced to predict the next event from the graph. the neural advisor is trained to model the conditional probability between event nodes and the context, including the input and previously predicted events, so that it can predict the next individual event rather than the entire sequence, thus enhancing controllability. finally, the predicted event sequence is sent to a downstream model for story generation. to the best of our knowledge, this is the first attempt to employ an unsupervised graph-based inference approach with a neural advisor as the event planning framework. a range of experiments are conducted to evaluate the performance of our approach, both on the quality of event sequences and their efficacy in aiding story generation. the results demonstrate that our model significantly outperforms all competitive baselines. 1 ## methodology the story generation task is formulated as follows: the given input is a sentence acting as the leading context @xmath0, c 2 , ..., c n } where c i denotes the i-th token of the leading context, and the output is a multi-sentence story @xmath1, s 1 2 , ..., s 2 1 ..., s m n }, where s i j denotes the j-th token of i-th sentence in a story. the task requires the prediction of an event sequence 2 @xmath2, e 2 , ..., e m } as a intermediate input, which is generated according to the leading context c and used to generate a story s. e i denotes the i-th event representing the i-th sentence in a story, and each event may have multiple tokens. ## conclusion this study proposes a novel hybrid event planning approach which performs inference on event graphs with the help of a neural event advisor. a range of experiments demonstrate that the proposed model outperforms other sota neural event planning approaches, and substantially improves performance on the downstream task of story generation.