G-Seek

ECAI-23

Although current summarization models can process increasingly long text sequences, they still struggle to capture salient related information spread across the extremely-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 long and multi-document summarization performance and accuracy across several datasets.

Keywords: long document summarization, abstractive summarization, low-resource, graph, NLP