# ALIGNMEET: A Comprehensive Tool for Meeting Annotation, Alignment, and Evaluation

arXiv: https://arxiv.org/abs/2205.05433 · HTML: https://mlsanity.com/paper/2205.05433/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 68 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 19 of 19)

- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [Asking and Answering Questions to Evaluate the Factual Consistency of Summaries](https://mlsanity.com/paper/2004.04228/) (2020)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Neural Text Summarization: A Critical Evaluation](https://mlsanity.com/paper/1908.08960/) (2019)
- [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://mlsanity.com/paper/2103.06410/) (2021)
- [Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts](https://mlsanity.com/paper/1809.05715/) (2018)
- [Guiding Extractive Summarization with Question-Answering Rewards](https://mlsanity.com/paper/1904.02321/) (2019)
- [Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes](https://mlsanity.com/paper/2009.06851/) (2020)
- [GameWikiSum: a Novel Large Multi-Document Summarization Dataset](https://mlsanity.com/paper/2002.06851/) (2020)
- [Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization](https://mlsanity.com/paper/2005.03510/) (2020)
- [A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification](https://mlsanity.com/paper/1710.02318/) (2017)
- [A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining](https://mlsanity.com/paper/2004.02016/) (2020)
- [Summarizing Utterances from Japanese Assembly Minutes using Political Sentence-BERT-based Method for QA Lab-PoliInfo-2 Task of NTCIR-15](https://mlsanity.com/paper/2010.12077/) (2020)
- [LIDA: Lightweight Interactive Dialogue Annotator](https://mlsanity.com/paper/1911.01599/) (2019)
- [Human or Machine: Automating Human Likeliness Evaluation of NLG Texts](https://mlsanity.com/paper/2006.03189/) (2020)

## Built on by (most cited first, 3 of 3)

- [Topic-Conversation Relevance (TCR) Dataset and Benchmarks](https://mlsanity.com/paper/2411.00038/) (2024)
- [FREDSum: A Dialogue Summarization Corpus for French Political Debates](https://mlsanity.com/paper/2312.04843/) (2023)
- [Minuteman: Machine and Human Joining Forces in Meeting Summarization](https://mlsanity.com/paper/2309.05272/) (2023)

## Abstract (arXiv)

Summarization is a challenging problem, and even more challenging is to manually create, correct, and evaluate the summaries. The severity of the problem grows when the inputs are multi-party dialogues in a meeting setup. To facilitate the research in this area, we present ALIGNMEET, a comprehensive tool for meeting annotation, alignment, and evaluation. The tool aims to provide an efficient and clear interface for fast annotation while mitigating the risk of introducing errors. Moreover, we add an evaluation mode that enables a comprehensive quality evaluation of meeting minutes. To the best of our knowledge, there is no such tool available. We release the tool as open source. It is also directly installable from PyPI.