# Knowledge-Centric Templatic Views of Documents

arXiv: https://arxiv.org/abs/2401.06945 · HTML: https://mlsanity.com/paper/2401.06945/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 42 references that are in the map.
- First posted on arXiv 13 Jan 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [Analyzing Leakage of Personally Identifiable Information in Language Models](https://mlsanity.com/paper/2302.00539/) (2023)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://mlsanity.com/paper/2105.06762/) (2021)
- [Fill in the BLANC: Human-free quality estimation of document summaries](https://mlsanity.com/paper/2002.09836/) (2020)
- [DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents](https://mlsanity.com/paper/2101.11796/) (2021)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [Knowledge-Aware Language Model Pretraining](https://mlsanity.com/paper/2007.00655/) (2020)
- [D2S: Document-to-Slide Generation Via Query-Based Text Summarization](https://mlsanity.com/paper/2105.03664/) (2021)
- [TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks](https://mlsanity.com/paper/1906.01351/) (2019)
- [Scientific Article Summarization Using Citation-Context and Article's Discourse Structure](https://mlsanity.com/paper/1704.06619/) (2017)
- [Neural Content Extraction for Poster Generation of Scientific Papers](https://mlsanity.com/paper/2112.08550/) (2021)
- [Learning to Generate Posters of Scientific Papers](https://mlsanity.com/paper/1604.01219/) (2016)
- [Echoes from Alexandria: A Large Resource for Multilingual Book Summarization](https://mlsanity.com/paper/2306.04334/) (2023)
- [SciBERTSUM: Extractive Summarization for Scientific Documents](https://mlsanity.com/paper/2201.08495/) (2022)

## Built on by (most cited first, 5 of 5)

- [PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides](https://mlsanity.com/paper/2501.03936/) (2025)
- [DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation](https://mlsanity.com/paper/2602.22839/) (2026)
- [PreGenie: An Agentic Framework for High-quality Visual Presentation Generation](https://mlsanity.com/paper/2505.21660/) (2025)
- [SlideTailor: Personalized Presentation Slide Generation for Scientific Papers](https://mlsanity.com/paper/2512.20292/) (2025)
- [DeepSlide: From Artifacts to Presentation Delivery](https://mlsanity.com/paper/2605.15202/) (2026)

## Abstract (arXiv)

Authors seeking to communicate with broader audiences often share their ideas in various document formats, such as slide decks, newsletters, reports, and posters. Prior work on document generation has generally tackled the creation of each separate format to be a different task, leading to fragmented learning processes, redundancy in models and methods, and disjointed evaluation. We consider each of these documents as templatic views of the same underlying knowledge/content, and we aim to unify the generation and evaluation of these templatic views. We begin by showing that current LLMs are capable of generating various document formats with little to no supervision. Further, a simple augmentation involving a structured intermediate representation can improve performance, especially for smaller models. We then introduce a novel unified evaluation framework that can be adapted to measuring the quality of document generators for heterogeneous downstream applications. This evaluation is adaptable to a range of user defined criteria and application scenarios, obviating the need for task specific evaluation metrics. Finally, we conduct a human evaluation, which shows that people prefer 82% of the documents generated with our method, while correlating more highly with our unified evaluation framework than prior metrics in the literature.