Challenge: Existing methods to extract summaries from Hindi textbooks perform poorly in English.
Approach: They propose to use unsupervised methods to extract single document summarization from Hindi history textbooks.
Outcome: The proposed tool could help students memorize a text summary for the exam . prior studies show that the proposed methods perform poorly on Hindi documents .

Similar Papers

Extractive Summarization with Text Generator (2024.naacl-long)

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Challenge: Existing extractive systems lack gold training signals, thereby hindering learning of extractive models.
Approach: They propose to use text generators to train extractive summarizers by approximating outputs of abstractive summaries.
Outcome: The proposed method can be used to train extractive summarizers without training . it is shown that the approximated summaries correlate positively with the auxiliary summary outputs.
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)

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Challenge: Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents.
Approach: They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents.
Outcome: The proposed model achieves state-of-the-art on unsupervised summarization and is less dependent on sentence positions.
Extractive Summarization via ChatGPT for Faithful Summary Generation (2023.findings-emnlp)

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Challenge: Abstractive summarization methods struggle with generating ungrammatical or even nonfactual contents.
Approach: They evaluate ChatGPT's performance on extractive summarization and compare it with traditional fine-tuning methods on benchmark datasets.
Outcome: The proposed pipeline performs better than abstractive methods on summary faithfulness and in-context learning.
SummVD : An efficient approach for unsupervised topic-based text summarization (2022.aacl-main)

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Challenge: SummVD is an unsupervised extractive summarization method that uses word clustering to reduce word embeddings.
Approach: They propose a method for automatic unsupervised extractive summarization using word clustering and singular value decomposition.
Outcome: The proposed method outperforms other extractive methods using several corpora of different nature.
Ranking Sentences for Extractive Summarization with Reinforcement Learning (N18-1)

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Challenge: Abstractive summarization involves various text rewriting operations and has been identified as a sequence-to-sequence problem.
Approach: They propose a novel algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective.
Outcome: The proposed algorithm outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.
Learning From the Source Document: Unsupervised Abstractive Summarization (2022.findings-emnlp)

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Challenge: Existing methods for abstractive summarization are under supervised training, but obtaining high-quality and large-scale datasets for supervised learning is laboriously difficult.
Approach: They propose an unsupervised method that leverages contrastive learning to generate summaries by rewriting and paraphrasing the source documents to generate good summary.
Outcome: The proposed method outperforms baseline methods on extensive experiments on source documents and fake documents.
Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)

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Challenge: Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries.
Approach: They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document.
Outcome: The proposed method improves extractive summarization performance on CNN/Daily Mail dataset.
Exploring Content Selection in Summarization of Novel Chapters (2020.acl-main)

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Challenge: We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summary summaries.
Approach: They propose a new metric for aligning summary sentences with chapter sentences to create gold extracts.
Outcome: The proposed method improves on previous methods and automatic metrics and a crowd-sourced pyramid analysis.
A Set Prediction Network For Extractive Summarization (2023.findings-acl)

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Challenge: Recent approaches to extracting salient sentences from source document are naive and lack dependencies between sentences.
Approach: They propose a set prediction network to detect redundancy relationship between sentences . they use a non-autoregressive decoder to predict sentences in parallel .
Outcome: The proposed method outperforms previous state-of-the-art models on extracted summary datasets.
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)

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Challenge: Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved.
Approach: They propose to use different types of model architectures to improve extractive summarization systems.
Outcome: The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis.

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