Unsupervised Abstractive Summarization of Bengali Text Documents (2021.eacl-main)
Copied to clipboard
Radia Rayan Chowdhury, Mir Tafseer Nayeem, Tahsin Tasnim Mim, Md. Saifur Rahman Chowdhury, Taufiqul Jannat
| Challenge: | Abstractive summarization systems are difficult to perform due to the unavailability of the parallel data for low-resource languages like Bengali. |
| Approach: | They propose a graph-based unsupervised abstractive summarization system in Bengali text documents that requires only a Part-Of-Speech (POS) tagger and a pre-trained language model trained on Bengali texts. |
| Outcome: | The proposed system outperforms baselines without human-annotated reference summaries on a human-random dataset with Bengali text. |
Similar Papers
Learning From the Source Document: Unsupervised Abstractive Summarization (2022.findings-emnlp)
Copied to clipboard
| 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. |
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion (C18-1)
Copied to clipboard
| Challenge: | a new method for abstractive summarization is being developed for document summarizing . abstractive methods require extensive natural language generation to rewrite the sentences . |
| Approach: | They propose an unsupervised abstractive summarization system in multi-document context . they use a paraphrastic sentence fusion model which performs sentence synthesis and paraphrazing . |
| Outcome: | The proposed model improves information coverage and abstractiveness of generated sentences. |
A Robust Abstractive System for Cross-Lingual Summarization (N19-1)
Copied to clipboard
| Challenge: | We present a novel system for cross-lingual summarization that can be applied to low-resource languages. |
| Approach: | They propose a neural abstractive summarization system that can be applied to low-resource languages . they use machine translation and the New York Times summarizing corpus to create a corpus . |
| Outcome: | The proposed system achieves higher fluency than standard summarizers on translated documents . the proposed system can be easily applied to new low-resource languages . |
Abstractive Document Summarization without Parallel Data (2020.lrec-1)
Copied to clipboard
| Challenge: | Abstractive summarization typically relies on large collections of paired articles and summaries. |
| Approach: | They propose a system that relies only on example summaries and non-matching articles . they use an unsupervised sentence extractor that selects salient sentences . |
| Outcome: | The proposed system performs well on CNN/DailyMail benchmark and automatic generating a press release from a scientific journal article. |
Abstractive Summarization of Bengali Academic Videos Based on Audio Subtitles (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for summarizing educational videos in Bengali are limited due to the rapid growth of educational video content. |
| Approach: | They propose an end-to-end pipeline for the abstractive summarization of Bengali videos . they fine-tuned the BanglaT5 model on a new benchmark dataset . |
| Outcome: | The proposed system preprocesses audio and converts speech to text using Google's Speech Recognition API. |
Unsupervised Semantic Abstractive Summarization (P18-3)
Copied to clipboard
| Challenge: | Existing methods for abstractive summarization are limited in the sense that they can never generate human level summaries for large and complicated documents. |
| Approach: | They propose a pipeline for automatic abstractive summary generation using co-reference resolution and Meta Nodes. |
| Outcome: | The proposed pipeline outperforms the state-of-the-art method by 1.7% in node prediction. |
Leveraging Graph to Improve Abstractive Multi-Document Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Empirical results show that our model brings substantial improvements over several strong baselines. |
| Approach: | They propose a neural abstractive multi-document summarization model which captures cross-document relations and can guide the summary generation process. |
| Outcome: | The proposed model improves on the WikiSum and MultiNews datasets and can be easily combined with pre-trained language models. |
On the Abstractiveness of Neural Document Summarization (D18-1)
Copied to clipboard
| Challenge: | Recent studies show that document summarization systems are abstractive . authors suggest that automated summarizing systems could be improved . |
| Approach: | They propose to use a pure copy system to verify abstractiveness of document summarization systems. |
| Outcome: | The proposed system produces abstractive summaries while being far more efficient. |
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking (P19-1)
Copied to clipboard
| Challenge: | Currently, unsupervised summarization is widely used for product reviews on E-commerce websites. |
| Approach: | They propose an unsupervised model that learns the latent discourse tree without an external parser and generates a concise summary. |
| Outcome: | The proposed model outperforms other unsupervised approaches for relatively long reviews and is competitive with or better than supervised models. |
Abstractive Summarizers are Excellent Extractive Summarizers (2023.acl-short)
Copied to clipboard
| Challenge: | Abstractive summarization systems have traditionally been fragmented, limiting the benefits of compatible models. |
| Approach: | They propose three new inference algorithms using sequence-to-sequence architectures to model extractive summarization with an abstractive summmarization system. |
| Outcome: | The proposed algorithms outperform existing models on CNN and Dailymail and show that they are more efficient than existing models. |