Design of a Knowledge-Based Report Generator

By K. Kukich. 1983

Knowledge-Based Report Generation is a technique for automatically generating natural language reports from computer databases. It is so named because it applies knowledge-based expert systems software to the problem of text generation. The first application of the technique, a system for generating natural language stock reports from a daily stock quotes database, is partially implemented. Three fundamental principles of the technique are its use of domain-specific semantic and linguistic knowledge, its use of macro-level semantic and linguistic constructs (such as whole messages, a phrasal lexicon, and a sentence-combining grammar), and its production system approach to knowledge representation.

Subfields: Natural Language Processing

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Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, background): “Data-to-Text Generation is the task of generating natural language descriptions conditioned on structured data [90, 127], such as tables [140, 207], database records [24], and knowledge graphs [54].”
BLEURT: Learning Robust Metrics for Text Generation (2020, background): “…tackle a wide array of tasks including translation (Koehn, 2009), summarization (Mani, 1999; Chopra et al., 2016), structured-data-to-text generation (McKeown, 1992; Kukich, 1983; Wiseman et al., 2017) dialog (Smith and Hipp, 1994; Vinyals and Le, 2015) and image cap-tioning (Fang et al., 2015).”
Challenges in Data-to-Document Generation (2017, background): “A classic problem in natural-language generation (NLG) (Kukich, 1983; McKeown, 1992; Reiter and Dale, 1997) involves taking structured data, such as a table, as input, and producing text”
ToTTo: A Controlled Table-To-Text Generation Dataset (2020, background): “Data-to-text generation (Kukich, 1983; McKeown, 1992) is the task of generating a target textual description y conditioned on source content x in the form of structured data such as a table.”
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation (2019, background): “The task of generating natural language descriptions of structured data (such as tables) (Kukich, 1983; McKeown, 1985; Reiter and Dale, 1997) has seen a growth in interest with the rise of sequence to sequence models that provide an easy way of encoding tables and generating text from them (Lebret…”
Text-to-Text Pre-Training for Data-to-Text Tasks (2020, background): “Natural language generation from structured data, or data-to-text (Kukich, 1983; McKeown, 1985), is the task of generating natural language text conditioned on source content provided in the form of structured data such as a table, graph etc.”
Data-to-Text Generation with Content Selection and Planning (2018, methodology): “The generation literature provides multiple examples of content selection components developed for various domains which are either hand-built (Kukich 1983; McKeown 1992; Reiter and Dale 1997; Duboue and McKeown 2003) or learned from data (Barzilay and Lapata 2005; Duboue and McKeown 2001; 2003; Lia”
Text generation (1990, methodology): “It was developped following ideas of Kittredge (1983) and reproduces in Prolog outputs similar to those obtained by Karen Kukich's ANA system described by Kukich (1983) .”
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation (2019, background): “…(Mani, 1999), where x is a long document and y is a more concise version, machine translation (Koehn, 2009), where x and y represent equivalent text in different languages, and data-to-text generation (Kukich, 1983; McKeown, 1992), where x is a structured table and y is a textual description.”
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation (2021, methodology): “Table-to-text Generation Table-to-text generation has been widely studied for decades (Kukich, 1983; Goldberg et al., 1994; Reiter and Dale, 1997).”
Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence (2020, methodology): “Each target text is split into meaningful fragments and aligned with corresponding data records, either by hand-engineered rules (Kukich, 1983; McKeown, 1992) or statistical induction (Liang et al., 2009; Koncel-Kedziorski et al., 2014; Qin et al., 2018).”
Text Generation with Exemplar-based Adaptive Decoding (2019, unknown): “, text summarization (Mani, 1999), machine translation (Koehn, 2009), and data-to-text generation (Kukich, 1983; McKeown, 1992; Reiter and Dale, 1997).”
Describing a Knowledge Base (2018, background): “18 induce templates and then fill appropriate content into slots (Kukich, 1983; Cawsey et al., 1997; Angeli et al., 2010; Duma and Klein, 2013; Konstas and Lapata, 2013a; Flanigan et al., 2016a).”
Learning Neural Templates for Text Generation (2018, methodology): “Traditional NLG systems (Kukich, 1983; McKeown, 1992; Belz, 2008; Gatt and Reiter, 2009), in contrast, largely avoid these problems.”
Mapping the field of Algorithmic Journalism (2016, background): “Other NLG applications are based on sports (Robin and McKeown 1996), medical (Portet et al. 2007), financial (Kukich 1983) or engineering data (Yu et al. 2007).”
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021, background): “Data-to-Text Generation Data-to-Text Generation has several benchmark datasets with slightly different objectives–WebNLG (Gardent et al., 2017) to convert a group of triples to text, E2ENLG (Dušek et al., 2018) (Etzioni et al., 2008; Angeli et al., 2015; Clancy et al., 2019) inherently create such a”