AMRTVS umm: AMR -augmented Hierarchical Network for TV Transcript Summarization (2022)

Abstract: This paper describes our AMRTVSumm system for the SummScreen datasets in the Automatic Summarization for Creative Writing shared task (Creative-Summ 2022). In order to capture the complicated entity interactions and dialogue structures in transcripts of TV series, we introduce a new Abstract Meaning Representation (AMR) (Banarescu et al., 2013), particularly designed to represent individual scenes in an episode. We also propose a new cross-level cross-attention mechanism to incorporate these scene AMRs into a hierarchical encoder-decoder baseline. On both the ForeverDreaming and TVMegaSite datasets of SummScreen, our system consistently outperforms the hierarchical transformer baseline. Compared with the state-of-the-art DialogLM (Zhong et al., 2021), our system still has a lower performance primarily because it is pretrained only on out-of-domain news data, unlike DialogLM, which uses extensive in-domain pretraining on dialogue and TV show data. Overall, our work suggests a promising direction to capture complicated long dialogue structures through graph representations and the need to combine graph representations with powerful pretrained language models.

## introduction abstractive summarization of tv show episodes aims to produce a summary from their transcripts or screenplays, capturing important plot development and character relations. for this shared task, we participated in the two summscreen categories, which involve abstractively summarizing primetime tv series (foreverdreaming) and daytime soap operas (tvmegasite) @xcite . this task presents several new challenges compared with other abstractive summarization tasks. first, transformer-based language models that perform well on shorter texts become computationally * equal contribution expensive when their self-attention is applied to long inputs @xcite . also, consecutive scenes often describe parallel or different subplots, making it difficult to integrate information and present a correct narrative @xcite . finally, like other dialogue texts such as meetings and media interviews, tv transcripts contain complicated character and entity interactions as well as more varied structures. works on long-document summarization have explored transformers with sparse or windowbased attention @xcite , hierarchical models @xcite , and the "retrieve-then-summarize" approach @xcite . large pretrained language models such as bart-large also give strong results by taking longer inputs at the cost of larger embeddings and increased computational complexity @xcite . however, despite the many works addressing the long transcript problem, few have studied novel approaches to model the complicated interactions and structures in tv transcripts. even the stateof-the-art on summscreen, dialoglm, relies on dialogue-specific denoising pretraining on tv data. the model architecture itself does not address tv transcripts' conversational structures and takes the input transcripts as plain texts @xcite . therefore, we propose a novel abstract meaning representation (amr) to capture the diverse entity interactions and complex structures of tv transcripts. amr, as a graph representation, captures the most salient semantic knowledge using its concept nodes and preserves inter-concept relations with its labeled edges. it is thus believed to convey information orthogonal to the text input @xcite . our work generalizes the sentence-level amr introduced by @xcite , adding new features to make them suitable for individual scenes of tv shows. we use these scene-level amrs to augment a hierarchical encoder-decoder baseline. to this end, we also propose a cross-level cross-attention to scene amrs, such that the encoder of local tokens and utterance embeddings can benefit from the structural information and higher-level semantics from the entire corresponding scene, without being interfered by an adjacent scene, which may focus on a parallel or different subplot. to sum up, the major contributions of our work are presented as follows: ## datasets we participated in the two summscreen categories of the creativesumm 2022 shared task: summarization of primetime television transcripts (foreverdreaming) and summarization of daytime "soap opera" transcripts (tvmegasite) @xcite . we primarily experimented with our system on foreverdreaming, since it includes more genres and covers 66 tv shows in the train set. we used its entire train set of 3673 episodes to train our model. for tvmegasite, we only used 6000 of its 18915 training episodes due to the time constraint. the 6000-episode subset was sampled from the original train set to include approximately the same number of episodes from each tv show. we still use the original dev and test sets for both foreverdreaming and tvmegasite. ## system overview our amr-augmented hierarchical summarization network consists of a hierarchical text encoder and a scene-level amr encoder. the system is illustrated in figure 2 . ## results at the time of our blind test submission to the shared task, we only trained our system on smaller subsets of the summscreen datasets. the resulting checkpoints therefore did not achieve a high performance on the original test sets of summscreen nor on the blind test sets provided by the shared task. we will analyze these results in section 6.3. here, we first present our more recent results from training on the expanded train sets after our blind test submission. specifically, we eventually used the complete train set for foreverdreaming and a re-sampled 6000-episode subset for tvmegasite. all results were reported on the original test sets without re-sampling. ## conclusion we describe our amrtvsumm system for the two summscreen datasets in the creativesumm 2022 shared task. based on our proposed scene amr graph and hierarchical architecture with cross-level cross-attention, our system achieves substantial improvement over its hierarchical baseline under the same out-of-domain pretraining. however, it still does not outperform the state-of-the-art model that relies on extensive in-domain pretraining. our work suggests that despite graph representation's power in modeling complicated dialogue structures, it does not replace the role of dialogue-specific and tv-specific pretraining. a promising future direction will be to leverage the advantages of both by augmenting a state-of-the-art pretrained language model with scene amrs.