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allsummarizer | Multilingual automatic text summarizer | Natural Language Processing library

 by   kariminf Java Version: v3.0.0 License: Apache-2.0

 by   kariminf Java Version: v3.0.0 License: Apache-2.0

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kandi X-RAY | allsummarizer Summary

allsummarizer is a Java library typically used in Artificial Intelligence, Natural Language Processing, Pytorch applications. allsummarizer has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Multilingual automatic text summarizer using statistical approach and extraction
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Quality
Quality
Security
Security
License
License
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kandi-support Support

  • allsummarizer has a low active ecosystem.
  • It has 26 star(s) with 11 fork(s). There are 5 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 2 open issues and 4 have been closed. On average issues are closed in 210 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of allsummarizer is v3.0.0
allsummarizer Support
Best in #Natural Language Processing
Average in #Natural Language Processing
allsummarizer Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • allsummarizer has 0 bugs and 0 code smells.
allsummarizer Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
allsummarizer Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

  • allsummarizer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • allsummarizer code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
allsummarizer Security
Best in #Natural Language Processing
Average in #Natural Language Processing
allsummarizer Security
Best in #Natural Language Processing
Average in #Natural Language Processing

license License

  • allsummarizer is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
allsummarizer License
Best in #Natural Language Processing
Average in #Natural Language Processing
allsummarizer License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • allsummarizer releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • allsummarizer saves you 3087 person hours of effort in developing the same functionality from scratch.
  • It has 6649 lines of code, 337 functions and 75 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
allsummarizer Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
allsummarizer Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

kandi has reviewed allsummarizer and discovered the below as its top functions. This is intended to give you an instant insight into allsummarizer implemented functionality, and help decide if they suit your requirements.

  • Sets the language .
    • Parse options .
      • Main method for testing purposes .
        • Create the similar classes .
          • Handles POST request .
            • Trains the cat .
              • compress input string
                • Open a file .
                  • Set the number of words sent by this document .
                    • Reorder the relatives .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      allsummarizer Key Features

                      Multilingual automatic text summarizer using statistical approach and extraction

                      allsummarizer Examples and Code Snippets

                      See all related Code Snippets

                      AllSummarizer

                      copy iconCopydownload iconDownload
                      @inproceedings {13-aries-al,
                      	author = {Aries, Abdelkrime and Oufaida, Houda and Nouali, Omar},
                      	title = {Using clustering and a modified classification algorithm for automatic text summarization},
                      	series = {Proc. SPIE},
                      	volume = {8658},
                      	number = {},
                      	pages = {865811-865811-9},
                      	year = {2013},
                      	doi = {10.1117/12.2004001},
                      	URL = { http://dx.doi.org/10.1117/12.2004001}
                      }
                      

                      Examples of command line

                      copy iconCopydownload iconDownload
                      exp
                      ├── multi
                      │   ├── M001
                      │   │   ├── M0010.english
                      │   │   ├── M0011.english
                      │   │   └── M0012.english
                      │   └── M002
                      │       ├── M0020.english
                      │       ├── M0021.english
                      │       └── M0022.english
                      └── single
                          ├── doc1.txt
                          └── doc2.txt
                      

                      single document examples:

                      copy iconCopydownload iconDownload
                      -i "exp/single" -o "exp/output" -l en -t "5-15:5" -n "100;200" -c -f "tfu,pos;tfb,rleng" -v
                      

                      multi-document examples:

                      copy iconCopydownload iconDownload
                      -i "exp/multi" -o "exp/output" -l en -t 5 -r "5;10" -c -f "tfu,pos" -v -m
                      

                      See all related Code Snippets

                      Community Discussions

                      Trending Discussions on Natural Language Processing
                      • number of matches for keywords in specified categories
                      • Apple's Natural Language API returns unexpected results
                      • Tokenize text but keep compund hyphenated words together
                      • Create new boolean fields based on specific bigrams appearing in a tokenized pandas dataframe
                      • ModuleNotFoundError: No module named 'milvus'
                      • Which model/technique to use for specific sentence extraction?
                      • Assigning True/False if a token is present in a data-frame
                      • How to calculate perplexity of a sentence using huggingface masked language models?
                      • Mapping values from a dictionary's list to a string in Python
                      • What are differences between AutoModelForSequenceClassification vs AutoModel
                      Trending Discussions on Natural Language Processing

                      QUESTION

                      number of matches for keywords in specified categories

                      Asked 2022-Apr-14 at 13:32

                      For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.

                      As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.

                      df_texts <- tibble(
                        text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the 
                        grasshopper"),
                        mammals=NA,
                        reptiles=NA,
                        birds=NA,
                        insects=NA
                      )
                      
                      df_animals <- tibble(animals=c("ape", "fox", "tortoise", "hare", "owl", "grasshopper"),
                                 type=c("mammal", "mammal", "reptile", "mammal", "bird", "insect"))
                      

                      So my desired result would be:

                      df_result <- tibble(
                        text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the 
                        grasshopper"),
                        mammals=c(2,1,0),
                        reptiles=c(0,1,0),
                        birds=c(0,0,1),
                        insects=c(0,0,1)
                      )
                      

                      Is there a straightforward way to achieve this keyword-matching-and-counting that would be applicable to a much larger dataset?

                      Thanks in advance!

                      ANSWER

                      Answered 2022-Apr-14 at 13:32

                      Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.

                      library(tidyverse)
                      
                      cbind(df_texts[, 1], sapply(df_animals$animals, grepl, df_texts$text)) %>% 
                        pivot_longer(-text, names_to = "animals") %>% 
                        left_join(df_animals) %>% 
                        group_by(text, type) %>% 
                        summarise(sum = sum(value)) %>% 
                        pivot_wider(id_cols = text, names_from = type, values_from = sum)
                      
                        text                                   bird insect mammal reptile
                        <chr>                                 <int>  <int>  <int>   <int>
                      1 "the ape and the fox"                     0      0      2       0
                      2 "the owl and the the \n  grasshopper"     1      0      0       0
                      3 "the tortoise and the hare"               0      0      1       1
                      

                      To account for the several occurrences per text:

                      cbind(df_texts[, 1], t(sapply(df_texts$text, str_count, df_animals$animals, USE.NAMES = F))) %>% 
                        setNames(c("text", df_animals$animals)) %>% 
                        pivot_longer(-text, names_to = "animals") %>% 
                        left_join(df_animals) %>% 
                        group_by(text, type) %>% 
                        summarise(sum = sum(value)) %>% 
                        pivot_wider(id_cols = text, names_from = type, values_from = sum)
                      

                      Source https://stackoverflow.com/questions/71871613

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install allsummarizer

                      You can download it from GitHub.
                      You can use allsummarizer like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the allsummarizer component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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