AI News Summarizer Spend less time reading news

INTRODUCTIONS

What is SUMMARIZER ?

AI News Summarizer - a fully automated newspaper.

Summarizer aims to make your daily news shorter by utilizing AI.

Its bots crawl the web for news, summarize them, and then sort them into categories.

It is a fully automated newspaper.

Summarizer aims to make your daily news shorter by utilizing AI.

Its bots crawl the web for news, summarize them, and then sort them into categories.

Summarizer is exclusive to $SMR holders.

You won't have to pay anything, just simply holding

$SMR to read Summarizer contents.

At any time, you decide to stop reading Summarizer, you can

just sell your $SMR back to the market.

Run but bots

But for the human

Summarizer is run by a family of bots .

There are crawler-bot, summa-bot,

editor-bot, delivery-bot, optimizing-bot,

repairing-bot, etc.

AI-Powered

Making your daily news shorter by

utilizing AI

We use TextRank with optimizations on

the similarity function for text

summarization.

Elegant, and fast

Optimized UX & UI

Come with dark & light modes, stripped

all the unnecessary elements, optimized

for speed. Articles on Summarizer

normally take less than 1s to be fully

loaded.

Telegram

You love Telegram, right?

Good news! There is a bot of the

Summarizer bots family, who is

specialized in delivery Summarizer

content to Telegram

No payment hassle

Cause there is no payment

There is no subscription fee, you simply

hold $SMR to read Summarizer. At any

time, you decide to stop reading

Summarizer, you can sell your $SMR

back to the market.

Privacy

No tracker on Summarizer

Unlike almost any other news website,

Summarizer doesn't have any piece of

code to track your identity and

behaviors.

Algorithm

TextRank is an unsupervised algorithm for the automated summarization of texts that can also be

used to obtain the most important keywords in a document. It was introduced by Rada Mihalcea

and Paul Tarau

The algorithm applies a variation of PageRank over a graph constructed specifically for the task of

summarization. This produces a ranking of the elements in the graph: the most important

elements are the ones that better describe the text. This approach allows TextRank to build

summaries without the need of a training corpus or labeling and allows the use of the algorithm

with different languages.

For the task of automated summarization, TextRank models any document as a graph using

sentences as nodes . A function to compute the similarity of sentences is needed to build edges in

between. This function is used to weight the graph edges, the higher the similarity between

sentences the more important the edge between them will be in the graph. In the domain of a

Random Walker, as used frequently in PageRank , we can say that we are more likely to go from

one sentence to another if they are very similar.

TextRank determines the relation of similarity between two sentences based on the content that

both share. This overlap is calculated simply as the number of common lexical tokens between

them, divided by the lenght of each to avoid promoting long sentences. The function featured in

the original algorithm can be formalized as:

Definition 1. Given Si

, Sj two sentences represented by a set of n words that in Si are represented

as Si = wi

, wi

, ..., wi

. The similarity function for Si, Sj can be defined as:

The result of this process is a dense graph representing the document. From this graph, PageRank

is used to compute the importance of each vertex. The most significative sentences are selected

and presented in the same order as they appear in the document as the summary.

These ideas are based in changing the way in which distances between sentences are computed

to weight the edges of the graph used for PageRank. These similarity measures are orthogonal to

the TextRank model, thus they can be easily integrated into the algorithm. We found some of these

variations to produce significative improvements over the original algorithm.

Longest Common Substring From two sentences we identify the longest common substring and

report the similarity to be its length

Cosine Distance The cosine similarity is a metric widely used to compare texts represented as

vectors. We used a classical TF-IDF model to represent the documents as vectors and computed

the cosine between vectors as a measure of similarity. Since the vectors are defined to be positive,

the cosine results in values in the range [0,1] where a value of 1 represents identical vectors and 0

represents orthogonal vectors .

BM25 BM25 / Okapi-BM25 is a ranking function widely used as the state of the art for Information

Retrieval tasks. BM25 is a variation of the TF-IDF model using a probabilistic model .

Definition 2. Given two sentences R, S, BM25 is defined as:

where k and b are parameters. We used k = 1.2 and b = 0.75. avgDL is the average length of the

sentences in our collection.

This function definition implies that if a word appears in more than half the documents of the

collection, it will have a negative value. Since this can cause problems in the next stage of the

algorithm, we used the following correction formula:

where ε takes a value between 0.5 and 0.30 and avgIDF is the average IDF for all terms. Other

corrective strategies were also tested, setting ε = 0 and using simpler modifications of the classic

IDF formula.

We also used BM25+, a variation of BM25 that changes the way long docu- ments are penalized .

Evaluation

We tested LCS, Cosine Sim, BM25 and BM25+ as different ways to weight the edges for the

TextRank graph. The best results were obtained using BM25 and BM25+ with the corrective

formula shown in equation 3. We achieved

an improvement of 2.92% above the original TextRank result using BM25 and ε = 0.25. The

following chart shows the results obtained for the different variations we proposed.

The result of Cosine Similarity was also satisfactory with a 2.54% improvement over the original

method. The LCS variation also performed better than the original TextRank algorithm with 1.40%

total improvement.

The performance in time was also improved. We could process the 567 documents from the

DUC2002 database in 84% of the time needed in the original version.

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https://medium.com/@summarizer

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Author

Forum Username: Landiva

Forum Profile Link: https://bitcointalk.org/index.php?action=profile;u=3325170

Telegram Username: @landiva

ETH Wallet Address: 0x8c7eED86A310f62B27f9C05aF3ee417Ad6A45a7d