Graph-Based Abstractive Summarization of Extracted Essential Knowledge for Low-Resource Scenarios

Although current summarization models can process increasingly long text sequences, they still struggle to capture salient related information spread across the lengthy size of inputs with few labeled training instances. Today’s research still relies on standard input truncation without considering graph-based modeling of multiple semantic units to summarize only crucial facets. This paper proposes G-SEEK, a graph-based summarization of extracted essential knowledge. By representing the long source with a heterogeneous graph, our method extracts and provides salient sentences to an abstractive summarization model to generate the summary. Experimental results in low-resource scenarios, distinguished by data scarcity, reveal that G-SEEK consistently improves both the long- and multi-document summarization performance and accuracy across several datasets.