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Found 15 papers, 4 papers with code
TWEETSUMM - A Dialog Summarization Dataset for Customer Service
In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue.
Extractive Summarization
Unsupervised Extractive Summarization
Using Question Answering Rewards to Improve Abstractive Summarization
Particularly, the results from human evaluations show that the summaries generated by our approach is preferred over 30% of the time over the summaries generated by general abstractive summarization models.
Label-Efficient Model Selection for Text Generation
Model selection for a given target task can be costly, as it may entail extensive annotation of the quality of outputs of different models.
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers
Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative.
Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization
We observe that there are different heuristics that are associated with summaries of different perspectives, and explore these heuristics to create weak-labeled data for intermediate training of the models before fine-tuning with scarce human annotated summaries.
TWEETSUMM -- A Dialog Summarization Dataset for Customer Service
In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue.
Extractive Summarization
Unsupervised Extractive Summarization
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS), which targets the use-case of generating actionable instructions from a set of sources.
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Document Summarization
on HowSumm-Step
Summary Grounded Conversation Generation
Many conversation datasets have been constructed in the recent years using crowdsourcing.
Corpus Wide Argument Mining -- a Working Solution
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic.
Financial Event Extraction Using Wikipedia-Based Weak Supervision
Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques.
Argument Invention from First Principles
In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic.
Controversy in Context
With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is.
Fast End-to-End Wikification
Wikification of large corpora is beneficial for various NLP applications.
Learning Concept Abstractness Using Weak Supervision
We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data.
Unsupervised corpus--wide claim detection
Automatic claim detection is a fundamental argument mining task that aims to automatically mine claims regarding a topic of consideration.
