Last updated on Apr 6, 2024
- All
- Analytical Techniques
Powered by AI and the LinkedIn community
1
Extractive summarization
2
Abstractive summarization
3
Hybrid summarization
4
Evaluation metrics
Text summarization is a natural language processing (NLP) technique that aims to generate concise and coherent summaries of long and complex texts. It can help you save time, extract key information, and improve your understanding of various topics. But what are the best NLP tools for text summarization and why? In this article, we will explore some of the most popular and effective tools and their features, benefits, and limitations.
Top experts in this article
Selected by the community from 13 contributions. Learn more
Earn a Community Top Voice badge
Add to collaborative articles to get recognized for your expertise on your profile. Learn more
-
8
-
5
- John Keith King White House Lead Communications Engineer, U.S. Dept of State, and Joint Chiefs of Staff in NMCC
2
1 Extractive summarization
Extractive summarization is a method that selects the most relevant sentences or phrases from the original text and combines them into a summary, preserving its structure and meaning. TextRank, LexRank, and Luhn are some of the best tools for extractive summarization; they rank sentences based on their similarity and importance, use cosine similarity and idf-modified cosine as metrics, and consider the frequency and position of keywords in the text, respectively. This method is fast, simple, and accurate but may produce redundant, incoherent, or incomplete summaries.
Help others by sharing more (125 characters min.)
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Stanford CoreNLP is a highly versatile suite of NLP tools that offers a wide range of functionalities, including tokenization & sentence relevance scoring based on syntactic & semantic analysis. Its extractive summarization capability is particularly useful in scenarios requiring deep linguistic analysis. In a project focused on summarizing academic papers, the ability of Stanford CoreNLP to understand complex sentence structures and hierarchies allowed for the extraction of key sentences that accurately represented the core arguments and findings of the papers.TextRank is not a tool per se but an algorithm that many NLP tools implement, including Gensim & some Python libraries specifically dedicated to summarizatio
LikeLike
Celebrate
Support
Love
Insightful
Funny
8
- John Keith King White House Lead Communications Engineer, U.S. Dept of State, and Joint Chiefs of Staff in NMCC
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
1. Extractive Summarization Frequency-based Algorithms: Methods like TF-IDF (Term Frequency-Inverse Document Frequency) Graph-based Models Algorithms like TextRank 2. Abstractive Summarization Sequence-to-Sequence Models (Seq2Seq) Transformer-based Models: Models like BERT, GPT-3, and T5 3. Hybrid Methods: -Combining extractive and abstractive approaches, hybrid methods first extract key sentences and then paraphrase them for a more natural summary4. Reinforcement Learning - Using reinforcement learning, models are trained to optimize for specific goals like readability and informativeness.
LikeLike
Celebrate
Support
Love
Insightful
Funny
2
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Extractive summarization is a technique that involves selecting the most important sentences or phrases from a text and combining them to create a summary that retains the original meaning and structure.This method is efficient and straightforward, but it can sometimes result in summaries that are redundant, incoherent, or incomplete.
LikeLike
Celebrate
Support
Love
Insightful
Funny
3
2 Abstractive summarization
Abstractive summarization is a method that generates new sentences that capture the main idea and details of the original text. This method uses natural language generation (NLG) techniques, such as paraphrasing, rephrasing, or generalization. BART, T5, and GPT-3 are some of the best tools for abstractive summarization. BART is a neural network model that relies on bidirectional encoder and autoregressive decoder to encode the text and generate the summary. T5 is a transformer-based model that uses text-to-text transfer learning to perform various NLP tasks, including summarization. GPT-3 is a large-scale language model that employs deep learning and self-attention to produce natural and fluent texts. Abstractive summarization offers more creativity, flexibility, and human-like results than other methods, but it can also introduce errors, biases, or inconsistencies.
Help others by sharing more (125 characters min.)
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
The Longformer extends the standard Transformer model by incorporating a global attention mechanism, allowing it to process significantly longer texts. This capability is particularly useful for abstractive summarization tasks involving lengthy documents where crucial information is spread across the text. The Longformer's design enables it to maintain context over extended passages, making it adept at generating coherent and comprehensive summaries that encapsulate the essence of the entire document.XLNet is a generalized autoregressive pretraining model that outperforms BERT on several benchmarks by leveraging the best of both autoregressive (like GPT) and autoencoding (like BERT) models.
LikeLike
Celebrate
Support
Love
Insightful
Funny
4
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Abstractive summarization is a method of summarizing text that involves generating new sentences to capture the key information from the original text, rather than simply selecting and rearranging existing sentences. This approach aims to produce more concise and coherent summaries that better capture the meaning and intent of the original text. Abstractive summarization methods often use natural language processing (NLP) techniques, such as machine learning algorithms and language models, to generate the summaries. While abstractive summarization can produce more readable and informative summaries than extractive methods, it can also be more challenging and may require more advanced NLP techniques.
LikeLike
Celebrate
Support
Love
Insightful
Funny
4
Load more contributions
3 Hybrid summarization
Hybrid summarization is a method that combines extractive and abstractive techniques to produce summaries that are both informative and concise. This approach can leverage the strengths of both methods while overcoming their weaknesses. Some of the most effective tools for hybrid summarization are PreSumm, Pointer-Generator, and UniLM. Hybrid summarization is more balanced, diverse, and comprehensive, though it may require more computational resources and data than other methods.
Help others by sharing more (125 characters min.)
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Hybrid summarization combines extractive and abstractive techniques to create summaries that are both informative and concise. This approach leverages the strengths of both methods while addressing their weaknesses. This method may require more computational resources and data compared to extractive or abstractive summarization alone.
LikeLike
Celebrate
Support
Love
Insightful
Funny
5
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Cluster-based summarization techniques group similar sentences together & select representative sentences from each cluster. This method is effective for large datasets but may struggle with highly diverse content. In a customer review summarization task, cluster-based summarization helped identify common themes in reviews & generate summaries based on recurring opinions.BERT models combined with reinforcement learning can optimize the summarization process by fine-tuning the generation to maximize specific objectives. This approach requires careful tuning but can yield high-quality results. In a real-time news summarization system, BERT with reinforcement learning was employed to prioritize urgent information & produce timely summaries.
LikeLike
Celebrate
Support
Love
Insightful
Funny
4
- Rich Heimann Generative AI: MORE THAN YOU ASKED FOR is now available.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Pointer-generator networks combine concepts of extractive and abstractive summaries. Pointer-generator networks use a pointer mechanism to copy words from input text and generate new terms using a learned vocabulary.
LikeLike
Celebrate
Support
Love
Insightful
Funny
2
4 Evaluation metrics
Evaluation metrics are methods used to measure the quality and performance of text summarization tools. These metrics can be divided into two categories: intrinsic and extrinsic. Intrinsic metrics compare the summary with the original text or a reference summary, while extrinsic metrics assess the impact of the summary on a specific task or goal. Popular metrics for text summarization include ROUGE, which calculates the overlap of n-grams, word sequences, or LCS between the summary and the reference; BLEU, which computes the precision of n-grams between the summary and the reference with a penalty for brevity; and BERTScore, which uses BERT embeddings to measure the semantic similarity between the summary and the reference. Evaluation metrics can be useful in providing an objective and standardized assessment of summarization tools, however they may not capture all nuances, coherence, or relevance of a summary.
Help others by sharing more (125 characters min.)
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Content Coverage metric assesses how well the summary covers all the important points in the source text. Tools that have high content coverage are preferable for comprehensive summarization needs, such as in legal or technical document summarization.Latent Semantic Analysis (LSA) can be used to measure the semantic similarity between the source text and the summary. This is particularly useful for evaluating how well the summarization tool captures the underlying meanings and themesUltimately, the utility of a summarization tool is also determined by end-user satisfaction. User feedback on the summaries' usefulness, readability, and overall satisfaction provides valuable insights into the tool's effectiveness in practical applications.
LikeLike
Celebrate
Support
Love
Insightful
Funny
5
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
It is worth noting that not all spoken languages are supported by the various BERT based models, and then not all are mutli-lingual.BERTScore is a useful evaluation metric, however make sure that ir is applicable to your usecase
LikeLike
Celebrate
Support
Love
Insightful
Funny
1
Analytical Techniques
Analytical Techniques
+ Follow
Rate this article
We created this article with the help of AI. What do you think of it?
It’s great It’s not so great
Thanks for your feedback
Your feedback is private. Like or react to bring the conversation to your network.
Tell us more
Tell us why you didn’t like this article.
If you think something in this article goes against our Professional Community Policies, please let us know.
We appreciate you letting us know. Though we’re unable to respond directly, your feedback helps us improve this experience for everyone.
If you think this goes against our Professional Community Policies, please let us know.
More articles on Analytical Techniques
No more previous content
- What are some of the challenges or limitations of using principal component analysis for data analysis? 13 contributions
- How do you use text similarity measures to enhance your text mining and text analytics skills? 5 contributions
- What are the pros and cons of using cosine similarity vs. Jaccard similarity for text analysis? 6 contributions
- How do you incorporate user feedback and preferences in text summarization systems? 11 contributions
- How do you develop your data visualization skills and portfolio? 6 contributions
- What are some of the current research trends and directions in text summarization? 4 contributions
No more next content
More relevant reading
- Machine Learning How can you fine-tune a pre-trained NLP model for a specific use case?
- Machine Learning How can you effectively evaluate an NLP model for image captioning?
- Natural Language Processing How do stemming and lemmatization affect the performance and scalability of NLP applications?
- Machine Learning What are the most effective state-of-the-art NLP models for specific tasks?
Help improve contributions
Mark contributions as unhelpful if you find them irrelevant or not valuable to the article. This feedback is private to you and won’t be shared publicly.
Contribution hidden for you
This feedback is never shared publicly, we’ll use it to show better contributions to everyone.