What are all papers about text summarization in 2017?
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Text summarization was a popular topic in 2017. Several papers explored different approaches and techniques for summarizing text. Tang et al. proposed a two-stage filter system for real-time summarization, using methods such as negative KL-divergence and cosine distance for text similarity modeling . Patel et al. discussed both extractive and abstractive summarization strategies, including TF-IDF, graph theory, and machine learning approaches . AbuRa’ed et al. presented their work on text summarization at the CL-SciSumm 2017 conference . Narang et al. introduced a genetic algorithm-based approach for text summarization, aiming to create less redundant summaries that cover all topics in the document . Rautray and Balabantaray proposed a nature-inspired cuckoo search optimization algorithm for optimal sentence selection in text summarization, achieving significant results in multi-document summarization .
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| Papers (4) | Insight |
|---|---|
| The provided paper is about text summarization using genetic algorithm. It does not mention any other papers about text summarization in 2017. | |
Open access•Proceedings Article 01 Jan 2017 | The provided paper is about the PKUICST's approaches and results in the Real-Time Summarization track at TREC 2017. It does not provide information about other papers on text summarization in 2017. |
Open access•Proceedings Article 01 Jan 2017 | The provided paper is about the LaSTUS/TALN system presented at CL-SciSumm 2017. It does not provide information about other papers on text summarization in 2017. |
| The provided paper does not mention any specific papers about text summarization in 2017. |
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