Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization

Understand

Neural models have become successful at producing abstractive summaries that are human-readable and fluent.

• However, these models have two critical shortcomings: they often don't respect the facts that are either included in the source article or are known to humans as commonsense knowledge, and they don't produce coherent summaries when the source article is long.
• In this work, we propose a novel architecture that extends Transformer encoder-decoder architecture in order to improve on these shortcomings.
• First, we incorporate entity-level knowledge from the Wikidata knowledge graph into the encoder-decoder architecture.