Untitled

Abstract

Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, we address the problem of generating coherent multi-sentence texts from the output of an information extraction system, and in particular a knowledge graph. Graphical knowledge representations are ubiquitous in computing, but pose a significant challenge for text generation techniques due to their non-hierarchical nature, collapsing of longdistance dependencies, and structural variety. We introduce a novel graph transforming encoder which can leverage the relational structure of such knowledge graphs without imposing linearization or hierarchical constraints. Incorporated into an encoder-decoder setup, we provide an end-to-end trainable system for graph-to-text generation that we apply to the domain of scientific text. Automatic and human evaluations show that our technique produces more informative texts which exhibit better document structure than competitive encoder-decoder methods. 1

Sentence-Level Content Planning and Style Specification for Neural Text Generation

“…We further consider a task of generating abstracts for scientific papers (Ammar et al, 2018), where the input contains a paper title and scientific entities mentioned in the abstract. We use the AGENDA data processed by Koncel-Kedziorski et al (2019), where entities and their relations in the abstracts are extracted by SciIE (Luan et al, 2018). All entities appearing in the abstract are included in our keyphrase bank.…”

“…For abstract generation, we compare with the state-of-the-art system GRAPHWRITER (Koncel-Kedziorski et al, 2019), which is a transformer model enabled with knowledge graph encoding mechanism to handle both the entities and their structural relations from the input.…”

Generating Accurate Electronic Health Assessment from Medical Graph

“…Due to the success of transformer model in applications such as machine translation and graph neural network, there is a recent trend to generate longer text (such as paragraph-level text) from structured data. Our work is most similar to (Koncel-Kedziorski et al, 2019), which further introduced a graph to text task by collecting 40k Semantic Scholar Corpus taken from the proceedings of AI conferences. Given a knowledge graph constructed by an automatic information extraction system and a scientific article's title, the goal is to generate a corresponding abstract.…”

Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension

“…Therefore, in very recent work Koncel-Kedziorski et al (2019) have used a self-attention encoder as a graph encoder for text generation, in a dual encoder model. A dual-encoder model similar to Koncel-Kedziorski et al (2019) is suitable for a setting where the input is knowledge from a graph knowledge-base. For a text-based setting like ours, where word order is important and the tokens are part of semantic arguments, an approach that tries to encode linguistic information in the same architecture (Strubell et al, 2018) is more appropriate.…”