@inproceedings{pawar-etal-2023-evaluation,
title = "Evaluation Metrics for Depth and Flow of Knowledge in Non-fiction Narrative Texts",
author = "Pawar, Sachin and
Palshikar, Girish and
Jain, Ankita and
Singh, Mahesh and
Rangarajan, Mahesh and
Agarwal, Aman and
Kumar, Vishal and
Singh, Karan",
editor = "Akoury, Nader and
Clark, Elizabeth and
Iyyer, Mohit and
Chaturvedi, Snigdha and
Brahman, Faeze and
Chandu, Khyathi",
booktitle = "Proceedings of the 5th Workshop on Narrative Understanding",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wnu-1.4",
doi = "10.18653/v1/2023.wnu-1.4",
pages = "16--24",
abstract = "In this paper, we describe the problem of automatically evaluating quality of knowledge expressed in a non-fiction narrative text. We focus on a specific type of documents where each document describes a certain technical problem and its solution. The goal is not only to evaluate the quality of knowledge in such a document, but also to automatically suggest possible improvements to the writer so that a better knowledge-rich document is produced. We propose new evaluation metrics to evaluate quality of knowledge contents as well as flow of different types of sentences. The suggestions for improvement are generated based on these metrics. The proposed metrics are completely unsupervised in nature and they are derived from a set of simple corpus statistics. We demonstrate the effectiveness of the proposed metrics as compared to other existing baseline metrics in our experiments.",
}
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<abstract>In this paper, we describe the problem of automatically evaluating quality of knowledge expressed in a non-fiction narrative text. We focus on a specific type of documents where each document describes a certain technical problem and its solution. The goal is not only to evaluate the quality of knowledge in such a document, but also to automatically suggest possible improvements to the writer so that a better knowledge-rich document is produced. We propose new evaluation metrics to evaluate quality of knowledge contents as well as flow of different types of sentences. The suggestions for improvement are generated based on these metrics. The proposed metrics are completely unsupervised in nature and they are derived from a set of simple corpus statistics. We demonstrate the effectiveness of the proposed metrics as compared to other existing baseline metrics in our experiments.</abstract>
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%0 Conference Proceedings
%T Evaluation Metrics for Depth and Flow of Knowledge in Non-fiction Narrative Texts
%A Pawar, Sachin
%A Palshikar, Girish
%A Jain, Ankita
%A Singh, Mahesh
%A Rangarajan, Mahesh
%A Agarwal, Aman
%A Kumar, Vishal
%A Singh, Karan
%Y Akoury, Nader
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Chaturvedi, Snigdha
%Y Brahman, Faeze
%Y Chandu, Khyathi
%S Proceedings of the 5th Workshop on Narrative Understanding
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F pawar-etal-2023-evaluation
%X In this paper, we describe the problem of automatically evaluating quality of knowledge expressed in a non-fiction narrative text. We focus on a specific type of documents where each document describes a certain technical problem and its solution. The goal is not only to evaluate the quality of knowledge in such a document, but also to automatically suggest possible improvements to the writer so that a better knowledge-rich document is produced. We propose new evaluation metrics to evaluate quality of knowledge contents as well as flow of different types of sentences. The suggestions for improvement are generated based on these metrics. The proposed metrics are completely unsupervised in nature and they are derived from a set of simple corpus statistics. We demonstrate the effectiveness of the proposed metrics as compared to other existing baseline metrics in our experiments.
%R 10.18653/v1/2023.wnu-1.4
%U https://aclanthology.org/2023.wnu-1.4
%U https://doi.org/10.18653/v1/2023.wnu-1.4
%P 16-24
Markdown (Informal)
[Evaluation Metrics for Depth and Flow of Knowledge in Non-fiction Narrative Texts](https://aclanthology.org/2023.wnu-1.4) (Pawar et al., WNU 2023)
ACL
- Sachin Pawar, Girish Palshikar, Ankita Jain, Mahesh Singh, Mahesh Rangarajan, Aman Agarwal, Vishal Kumar, and Karan Singh. 2023. Evaluation Metrics for Depth and Flow of Knowledge in Non-fiction Narrative Texts. In Proceedings of the 5th Workshop on Narrative Understanding, pages 16–24, Toronto, Canada. Association for Computational Linguistics.